public class CustomAspect
extends java.lang.Object
AllureLifecycle
,
Aspect
,
Pointcut
,
Around
| Modifier and Type | Field and Description |
|---|---|
private static io.qameta.allure.AllureLifecycle |
lifecycle
Allure context variable
|
| Constructor and Description |
|---|
CustomAspect() |
| Modifier and Type | Method and Description |
|---|---|
void |
anyPageMethod()
Defines pointcut for aspect.
|
void |
anyTestExampleMethod()
Defines pointcut for aspect.
|
void |
anyTestMethod()
Defines pointcut for aspect.
|
private static java.lang.String |
arrayToString(java.lang.Object... array)
Create a new AllureLifecycle instance if it's not created before and store in private static variable
|
static io.qameta.allure.AllureLifecycle |
getLifecycle()
Create a new AllureLifecycle instance if it's not created before and store in private static variable
|
java.lang.Object |
step(org.aspectj.lang.ProceedingJoinPoint joinPoint)
Describes implementation of aspect method instead of any method in pages and tests packages.
|
private static io.qameta.allure.AllureLifecycle lifecycle
public static io.qameta.allure.AllureLifecycle getLifecycle()
private static java.lang.String arrayToString(java.lang.Object... array)
array
- Array tham must be converted to stringpublic void anyPageMethod()
public void anyTestMethod()
public void anyTestExampleMethod()
public java.lang.Object step(org.aspectj.lang.ProceedingJoinPoint joinPoint) throws java.lang.Throwable
joinPoint
- method's pointer which replaced by aspect's method stepjava.lang.Throwable
- in case of error


ALTER VIEW name ALTER [ COLUMN ] column SET DEFAULT expression ALTER VIEW name ALTER [ COLUMN ] column DROP DEFAULT ALTER VIEW name OWNER TO new_owner ALTER VIEW name RENAME TO new_name ALTER VIEW name SET SCHEMA new_schema
ALTER VIEW changes various auxiliary properties of a view. (If you want to modify the view's defining query, use CREATE OR REPLACE VIEW.)
You must own the view to use ALTER VIEW. To change a view's schema, you must also have CREATE privilege on the new schema. To alter the owner, you must also be a direct or indirect member of the new owning role, and that role must have CREATE privilege on the view's schema. (These restrictions enforce that altering the owner doesn't do anything you couldn't do by dropping and recreating the view. However, a superuser can alter ownership of any view anyway.)
The name (optionally schema-qualified) of an existing view.
These forms set or remove the default value for a column. A default value associated with a view column is inserted into INSERT statements on the view before the view's ON INSERT rule is applied, if the INSERT does not specify a value for the column.
The user name of the new owner of the view.
The new name for the view.
The new schema for the view.


Provides functions for the Bayesian analysis of some simple commonly-used models, without using Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling. The 'rust' package <https://cran.r-project.org/package=rust> is used to simulate a random sample from the required posterior distribution. At the moment three conjugate hierarchical models are available: beta-binomial, gamma-Poisson and a 1-way analysis of variance (ANOVA).
| Version: | 1.0.0 |
| Depends: | R (≥ 3.3.1) |
| Imports: | bayesplot (≥ 1.1.0), graphics, methods, rust (≥ 1.2.2), stats |
| Suggests: | ggplot2 (≥ 2.2.1), knitr, rmarkdown, testthat |
| Published: | 2017-11-20 |
| Author: | Paul J. Northrop [aut, cre, cph], Benjamin D. Hall [aut, cph] |
| Maintainer: | Paul J. Northrop <p.northrop at ucl.ac.uk> |
| BugReports: | http://github.com/paulnorthrop/bang/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | http://github.com/paulnorthrop/bang |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | bang results |
| Reference manual: | bang.pdf |
| Vignettes: |
Hierarchical 1-way Analysis of Variance Conjugate Hierarchical Models Posterior Predictive Checking Introducing bang: Bayesian Analysis, No Gibbs |
| Package source: | bang_1.0.0.tar.gz |
| Windows binaries: | r-devel: bang_1.0.0.zip, r-release: bang_1.0.0.zip, r-oldrel: bang_1.0.0.zip |
| OS X binaries: | r-release: bang_1.0.0.tgz, r-oldrel: bang_1.0.0.tgz |
| Reverse suggests: | rust |
Please use the canonical form https://CRAN.R-project.org/package=bang to link to this page.


A set that considers only object id() for uniqueness.
This strategy has edge cases for builtin types- it’s possible to have two ‘foo’ strings in one of these sets, for example. Use sparingly.
Dictionary with ‘squishy’ removal of least recently used items.
tuple() subclass that adds labeled names.
Is also pickleable.
A dict that returns keys/values/items in the order they were added.
An object that maintains the order in which attributes are set upon it.
Also provides an iterator and a very basic getitem/setitem interface to those attributes.
(Not really a dict, since it iterates over values, not keys. Not really a list, either, since each value must have a key associated; hence there is no append or extend.)
A dict which populates missing values via a creation function.
Note the creation function takes a key, unlike collections.defaultdict.
A Registry that can store one or multiple instances of a single class on a per-thread scoped basis, or on a customized scope.
Appends items to a collection ensuring uniqueness.
Additional appends() of the same object are ignored. Membership is determined by identity (is a) not equality (==).
A WeakKeyDictionary with an object identity index.
Adds a .by_id dictionary to a regular WeakKeyDictionary. Trades performance during mutation operations for accelerated lookups by id().
The usual cautions about weak dictionaries and iteration also apply to this subclass.
Ensure basic interface compliance for an instance or dict of callables.
Checks that obj implements public methods of cls or has members listed in methods. If required is not supplied, implementing at least one interface method is sufficient. Methods present on obj that are not in the interface are ignored.
If obj is a dict and dict does not meet the interface requirements, the keys of the dictionary are inspected. Keys present in obj that are not in the interface will raise TypeErrors.
Raises TypeError if obj does not meet the interface criteria.
In all passing cases, an object with callable members is returned. In the simple case, obj is returned as-is; if dict processing kicks in then an anonymous class is returned.
Return an unordered sequence of all classes related to cls.
Traverses diamond hierarchies.
Fibs slightly: subclasses of builtin types are not returned. Thus class_hierarchy(class A(object)) returns (A, object), not A plus every class systemwide that derives from object.
Old-style classes are discarded and hierarchies rooted on them will not be descended.
A decorator that behaves like @property except that operates on classes rather than instances.
This is helpful when you need to compute __table_args__ and/or __mapper_args__ when using declarative.
If ‘key’ is present in dict ‘kw’, coerce its value to type ‘type_’ if necessary. If ‘flexi_bool’ is True, the string ‘0’ is considered false when coercing to boolean.
decode a slice object as sent to __getitem__.
takes into account the 2.5 __index__() method, basically.
A signature-matching decorator factory.
Decorates a function and issues a deprecation warning on use.
Return a (key, value) iterator for almost any dict-like object.
Given an instance or class, guess if it is or is acting as one of the basic collection types: list, set and dict. If the __emulates__ property is present, return that preferentially.
Given an iterator of which further sub-elements may also be iterators, flatten the sub-elements into a single iterator.
format_argspec_plus with considerations for typical __init__ methods
Wraps format_argspec_plus with error handling strategies for typical __init__ cases:
object.__init__ -> (self)
other unreflectable (usually C) -> (self, *args, **kwargs)
Returns a dictionary of formatted, introspected function arguments.
A enhanced variant of inspect.formatargspec to support code generation.
Returns:
Example:
>>> format_argspec_plus(lambda self, a, b, c=3, **d: 123)
{'args': '(self, a, b, c=3, **d)',
'self_arg': 'self',
'apply_kw': '(self, a, b, c=c, **d)',
'apply_pos': '(self, a, b, c, **d)'}
Return a function with a given __name__.
Will assign to __name__ and return the original function if possible on the Python implementation, otherwise a new function will be constructed.
Return the full set of inherited kwargs for the given cls.
Probes a class’s __init__ method, collecting all named arguments. If the __init__ defines a **kwargs catch-all, then the constructor is presumed to pass along unrecognized keywords to it’s base classes, and the collection process is repeated recursively on each of the bases.
Return the full set of legal kwargs for the given func.
inspect.getargspec with considerations for typical __init__ methods
Wraps inspect.getargspec with error handling for typical __init__ cases:
object.__init__ -> (self)
other unreflectable (usually C) -> (self, *args, **kwargs)
iterate all the keys and attributes associated with a class, without using getattr().
Does not use getattr() so that class-sensitive descriptors (i.e. property.__get__()) are not called.
Decorate a method memoize its return value.
Best applied to no-arg methods: memoization is not sensitive to argument values, and will always return the same value even when called with different arguments.
A read-only @property that is only evaluated once.
Automates delegation of __specials__ for a proxying type.
Decorates a function and issues a pending deprecation warning on use.
alias of PopulateDict
Turn an instancemethod into a (parent, name) pair to produce a serializable callable.
Assign a ‘_creation_order’ sequence to the given instance.
This allows multiple instances to be sorted in order of creation (typically within a single thread; the counter is not particularly threadsafe).
A constant symbol.
>>> symbol('foo') is symbol('foo')
True
>>> symbol('foo')
<symbol 'foo>
A slight refinement of the MAGICCOOKIE=object() pattern. The primary advantage of symbol() is its repr(). They are also singletons.
Repeated calls of symbol(‘name’) will all return the same instance.
Adjust the incoming callable such that a ‘self’ argument is not required.
Copy the given dict and update with the given values.
executes the given function, catches all exceptions and converts to a warning.


Settings¶
Gordon settings.yml
files are simple yaml files which define how Gordon
should behave.
Settings can be defined at two different levels, project and application level.
Resources such as lambdas or event sources can be defined in both levels, but there are some other settings which are only expected at project level.
General¶
These settings can be defined either at project or app level.
| Settings | Description |
|---|---|
lambdas |
Lambda definitions. Anatomy of Lambdas |
cron |
CloudWatch Events definitions. Anatomy of Cron integration |
dynamodb |
Dynamodb Event Source definitions. Anatomy of Dynamodb integration |
kinesis |
Kinesis Event Source definitions. Anatomy of Kinesis integration |
s3 |
S3 notfication definitions. Anatomy of S3 integration |
apigateway |
API Gateway definitions. Anatomy of Api Gateway integration |
Project settings¶
Project settings are defined in the root level of your project (project/settings.yml
). The section Project Anatomy will give you more information about how
you can customize your project.
Application settings¶
Application settings are defined within your applications (application/settings.yml
).
One particularity about application settings, is that those can be redefined as part of it’s initialization. If for example you want to reuse one application and redefining some settings, you can do so:
---
project: my-project
default-region: eu-west-1
code-bucket: my-bucket
apps:
- exampleapp
- exampleapp:
a: b
c: d
...


Distributed Web of Care
Class for Code Societies at SFPC
by Taeyoon Choi
A class at Code Societies Winter 2019 at the School for Poetic Computation in New York City.
Distributed Web of Care is an initiative to code to care and code carefully. In this class there will engage in three different activities. First, we will investigate centralized, decentralized, distributed and peer to peer networks. We will analyze the popular technology platforms, focusing on its network infrastructure and terms of services. We will compare them with decentralized alternatives such as Dat Peer to Peer Protocol and Scuttlebutt. Second, we will take on performative exercises to explore the feeling of being in a network. We will learn to move around in physical space with strings in order to care, not control, each other. Lastly, we will imagine the kinds of network we want for the future and we will discuss how we can build it with code and code of conduct. We will distribute our code and code of conduct as zines in physical print and data, published with Dat.
Suggested readings
- Reboot World by Paul Ford
- Meet Ray Johnson, the Greatest Artist You’ve Never Heard Of by Rachel Tashjian
- My website is a shifting house next to a river of knowledge. What could yours be? by Laurel Schwulst
- Dat Zine Library by Zach Mandeville
- Distributed Web of Care by Taeyoon Choi
- Ethics and Archiving the Web by Distributed Web of Care
- At the New York Tech Zine Fair, the Digital and the Tactile Converge by Megan N. Liberty
In class, we will use terminal, git and Dat. Please familiarize yourself with using the terminal. The class is based on a class from Summer 2018.
Code Societies
SFPC’s Code Societies, is a winter intensive session organized by Melanie Hoff. Code Societies will examine the ideological and corporeal attributes of computation; concentrating on the poetics and politics of culturally embedded software. How do different platforms and processes — including algorithms, data collection, social media, infrastructure, and interface — yield distinct modes of seeing, thinking, feeling, and reinforce existing systems of power? Through a balanced study of critical theory discussion and hands-on coding workshops, students will create small projects that explore and question these ideas.
Distributed Web of Care is an initiative to code to care and code carefully.
Distributed on Dat and GitHub.
2018
-
Dec 29, 2018
Residency at LACA
-
Dec 28, 2018
Class for Code Societies at SFPC
-
Dec 21, 2018
Building a Museum 353 Years in the Future
-
Sep 11, 2018
Finding Intimacy within Black Feminist Criticism
-
Jul 26, 2018
still stuck with words
-
Jul 26, 2018
Distributed Dance Floor
-
Jun 27, 2018
Distributed Web of Care Party
-
Jun 27, 2018
Skillshares: Peers in Practice
-
Jun 27, 2018
Communities and new infrastructures
-
Jun 27, 2018
New Gardens
-
May 20, 2018
Announcing Summer 2018 Fellows
-
Apr 28, 2018
Care shirt and hoodie
-
Apr 27, 2018
Artists in Residence at Ace Hotel New York
-
Apr 18, 2018
Ethics and Archiving the Web
-
Apr 18, 2018
Call for Fellows and Stewards
-
Apr 17, 2018
Code of Conduct
-
Mar 18, 2018
About
-
Distributed Web of Care


Run¶
Overview¶
The run
function is used for executing a mechanism, process or system. It can be called directly, however
it is typically invoked by calling the run
method of the object to be run. It executes an object by
calling the object’s execute
method. While an object’s execute
method can be called directly,
using its run
method is much easier because it:
- allows multiple rounds of execution to be run in sequence, whereas the
execute
method of an object runs only a single execution of the object;
- manages timing factors (such as updating the
CentralClock
and presentinginputs in the correct phase of execution of a system. .. * automatically aggregates results across executions and stores them in the results attribute of the object.
Understanding a few basic concepts about how the run
function operates will make it easier to use the
execute
and run
methods of PsyNeuLink objects. These are discussed below.
Scope of Execution¶
When the run
method of an object is called, it executes that object and all others within its scope of
execution. For a mechanism, the scope of execution is simply that mechanism. For a process,
the scope of execution is all of the mechanisms specified in its pathway
attribute. For a system,
the scope of execution is all of the mechanisms in the processes specified in the system’s
processes
attribute.
Timing¶
PsyNeuLink supports two time scales for executing objects: TIME_STEP
and
TRIAL
. Every mechanism defines how it is executed at one or both of these time
scales, and its current mode of execution is determined by its timeScale
attribute.
TIME_STEP
: this mode of execution is a mechanism’s closest approximation to continuous, or “real time” processing. Execution of atime_step
is defined as a single execution of all objects in the scope of execution at theirtime_step
time scale. Mechanisms called upon to execute atime_step
that do not support that time scale of execution have the option of generating an exception, being ignored, or providing their trial mode response, either on the firsttime_step
, everytime_step
, or the lasttime_step
in the sequence being run.
TRIAL
: this mode of execution is the “ballistic” execution of a mechanism to a state that would have been achieved withtime_step
execution to a specified criterion. The criterion can be specified in terms of the number oftime_steps
, or a condition to be met by the mechanism’s output. It is up to the mechanism how it implements itstrial
mode of execution (e.g., whether this is done by internal numerical iteration or an analytic calculation). Execution of atrial
is defined as the execution of atrial
of all of the objects in the scope of execution.
The time_scale
argument of an execute
or run
method determines the time scale for each
round of execution: a time_step
or a trial
. When a process is run, each mechanism is executed in the
order that it appears in the process’ pathway
, once per round of execution. When a system is run,
the order of execution is determined by the system’s executionList
attribute, which is based on the system’s
graph
(a list of the dependencies among all of the mechanisms in the system). Execution of the mechanisms in a
system also depends on the phaseSpec
of each mechanism: this determines when in an execution sequence it should
be executed. The CentralClock
is used to control timing, so executing a system
requires that it be appropriately updated.
The run
function handles all of the above factors automatically.
Inputs¶
The run
function presents the inputs for each round of execution to the inputStates of the relevant
mechanisms. These are specified in the inputs
argument of the execute
or run
method.
For a mechanism, they comprise the input value for each of the mechanism’s inputStates. For a process
or system, they comprise the input values for the inputState(s) of the ORIGIN
mechanism(s). Input values can be
specified in one of two ways: sequence format and mechanism format
.
Sequence format is more complex, but does not require the specification of mechanisms by name, and thus may better
suited for automated means of generating inputs. Mechanism format requires that inputs be assigned to mechanisms by
name, but is easier to use (as the order in which the mechanisms are specified does not matter). Both formats require
that inputs be specified as nested lists or ndarrays, that define the number of executions, mechanisms, inputStates
and elements of each input value. These factors determine the levels of nesting required for a list, or
the dimensionality (number of axes) for an ndarray. They are described below, followed by a description of the two
formats.
- Number of rounds of execution. If the
inputs
argument contains the input for more than one round of execution (i.e., multiple time_steps and/or trials), then the outermost level of the list, or axis 0 of the ndarray, is used for the rounds of execution, each item of which contains the set inputs for a given round. Otherwise, it is used for the next relevant factor in the list below. If the number of inputs specified is less than the number of executions, then the input list is cycled until the full number of executions is completed.
- Number of mechanisms. If
run
is used for a system, and it has more than oneORIGIN
mechanism, then the next level of nesting of a list, or next higher axis of an ndarray, is used for theORIGIN
mechanisms, with each item containing the inputs for a givenORIGIN
mechanism within a round. This factor is not relevant when run is used for a single mechanism, a process (which only ever has oneORIGIN
mechanism), or a system that has only oneORIGIN
mechanism. It is also not relevant for the mechanism format, since that separates the inputs for each mechanism into separate entries of a dictionary.
- Number of inputStates. In general, mechanisms have a single (“primary”) inputState; however, some types of
mechanisms can have more than one (see InputStates). If any
ORIGIN
mechanism in a process or system has more than one inputState, then the next level of nesting of a list, or next higher axis of an ndarray, is used for the set of inputStates for each mechanism.
- Number of elements for the value of an inputState. The input for an inputState can be a single element (e.g.,
a scalar) or have multiple elements (e.g., a vector). By convention, even if the input to an inputState is only a
single element, it should nevertheless always be specified as a list or a 1d np.array (it is internally converted to
the latter). PsyNeuLink can usually parse single-element inputs specified as a stand-alone value (e.g., as a number
not in a list or ndarray). Nevertheless, it is best to embed such inputs in a single-element list or a 1d array,
both for clarity and to insure consistent treatment of nested lists and ndarrays. If this convention is followed,
then the number of elements for a given input should not affect nesting of lists or dimensionality (number of axes)
of ndarrays of an
inputs
argument.
With these factors in mind, the inputs
argument can be specified in the simplest form possible (least
number of nestings for a list, or lowest dimension of an ndarray). It can be specified using one of two formats:
Sequence Format¶
(List[values] or ndarray) – this uses a nested list or ndarray to fully specify the input for
each round of execution in a sequence. It is more complex than the mechanism format,
and for systems requires that the inputs for each mechanism be specified in the same order in which those mechanisms
appear in the system’s originMechanisms
attribute. This is
generally the same order in which they are declared, and can be displayed using the system’s
show
method). Although this is format is more demanding, it may be better suited to
automated input generation, since it does not require that mechanisms be referenced explicitly (though it is
allowed). The following provides a description of the sequence format for all of the combinations of factors listed
above. The figure below shows examples.
Lists: if there is more than one round, then the outermost level of the list is used for the sequence of executions. If there is only one
ORIGIN
mechanism and it has only one inputState (the most common case), then a single sublist is used for the input of each round. If theORIGIN
mechanism has more than one inputState, then the entry for each round is a sublist of the inputStates, each entry of which is a sublist containing the input for that inputState. If there is more than one mechanism, but none have more than one inputState, then a sublist is used for each mechanism in each round, within which a sublist is used for the input for that mechanism. If there is more than one mechanism, and any have more than one inputState, then a sublist is used for each mechanism for each round, within which a sublist is used for each inputState of the corresponding mechanism, and inside that a sublist is used for the input for each inputState.ndarray: axis 0 is used for the first factor (round, mechanism, inputState or input) for which there is only one item, axis 1 is used for the next factor for which there is only one item, and so forth. For example, if there is more than one round, only one
ORIGIN
mechanism, and that has only one inputState (the most common case), then axis 0 is used for round, and axis 1 for inputs per round. In the extreme, if there are multiple rounds, more than oneORIGIN
mechanism, and more than one inputState for one or more of theORIGIN
mechanisms, then axis 0 is used for rounds, axis 1 for mechanisms within round, axis 2 for inputStates of each mechanism, and axis 3 for the input to each inputState of a mechanism. Note that if any mechanism being run (directly, or as one of theORIGIN
mechanisms of a process or system) has more than one inputState, then an axis must be committed to inputStates, and the input to every inputState of every mechanism must be specified in that axis (i.e., even for those mechanisms that have a single inputState).
Mechanism Format¶
(Dict[mechanism, List[values] or ndarray]) – this provides a simpler format for specifying inputs
than
the sequence format, and does not require that the inputs for each mechanism be specified in a particular order.
However, it requires that each mechanism that receives inputs be referenced explicitly (instead of by order),
which may be less suitable for automated forms of input generation. It uses a dictionary, each entry of which is the
sequence of inputs for an ORIGIN
mechanism; there must be one such entry for each of the ORIGIN
mechanisms of the
process or system being run. The key for each entry is the ORIGIN
mechanism, and the value contains either a list
or ndarray specifying the sequence of inputs for that mechanism, one for each round of execution. If a list is used,
and the mechanism has more than one inputState, then a sublist is used in each item of the list to specify the inputs
for each of the mechanism’s inputStates for that round. If an ndarray is used, axis 0 is used for the sequence of
rounds. If the mechanism has a single inputState, then axis 1 is used for the input for each round. If the mechanism
has multiple inputStates, then axis 1 is used for the inputStates, and axis 2 is used for the input to each
inputState for each round.
Initial Values¶
Any mechanism that is the sender
of a projection that closes a loop in a process or
system, and that is not an ORIGIN
mechanism, is designated as INITIALIZE_CYCLE
. An initial value can be assigned
to such mechanisms, that will be used to initialize the process or system when it is first run. These values are
specified in the initial_values
argument of run
, as a dictionary. The key for each entry must
be a mechanism designated as INITIALIZE_CYCLE
, and its value an input for the mechanism to be used as its initial
value. The size of the input (length of the outermost level if it is a list, or axis 0 if it is an np.ndarray),
must equal the number of inputStates of the mechanism, and the size of each value must match (in number and type of
elements) that of the variable
for the corresponding inputState.
Targets¶
If learning is specified for a process or system, then target values
for each round of execution must be provided for each TARGET
mechanism in the process or system being run. These
are specified in the targets
argument of the execute
or run
method, which can be in
any of three formats. The two formats used for inputs
(sequence and
mechanism format) can also be used for targets. However, the format of the lists or
ndarrays is simpler, since each TARGET
mechanism is assigned only a single target value, so there is never the need
for the extra level of nesting (or dimension of ndarray) used for inputStates in the specification of inputs
.
Details concerning the use of the sequence and
mechanism formats for targets is described below. Targets can also be specified
as a function (for example, to allow the target to depend on the outcome of processing).
If either the sequence or mechanism format is used, then the number of targets specified for each mechanism must
equal the number specified for the inputs
argument; as with inputs
, if the number of executions
specified is greater than the number of inputs (and targets), then the list will be cycled until the number of
executions specified is completed. If a function is used for the targets
, then it will be used to generate
a target for each round of execution.
The number of targets specified in the sequence or mechanism formats for each round of execution, or generated using
the function format, must equal the number of TARGET
mechanisms for the process or system being run (see process
targetMechanism
or
system targetMechanism
respectively), and the value of each target must
match (in number and type of elements) that of the target
attribute of the TARGET
mechanism for which it is intended. Furthermore, if a range is specified for the output of
the TERMINAL
mechanism with which the target is compared (that is, the mechanism that provides the
ComparatorMechanism's
sample
value, then the target must be within that range (for example, if the TERMINAL
mechanism is a
TransferMechanism that uses a Logistic
function, it’s range
is
[0,1], so the target must be within that range).
Sequence Format¶
(List[values] or ndarray): – there are at most three levels of nesting (or dimensions) required for
targets
: one for executions, one for mechanisms, and one for the elements of each input. For a system
with more than one TARGET
mechanism, the targets must be specified in the same order as they appear in the system’s
targetMechanisms
attribute. This should be the same order in which
they are declared, and can be displayed using the system’s show
method). All
other requirements are the same as the sequence format for inputs
.
Mechanism Format¶
(Dict[mechanism, List[values] or ndarray]): – there must be one entry in the dictionary for each of the TARGET
mechanisms in the process or system being run, though the entries can be specified in any order. For this reason,
this format may be easier (and safer) to use. The value of each entry is a list or ndarray of the target values for
that mechanism, one for each round of execution. There are at most two levels of nesting (or dimensions)
required for each entry: one for the execution, and the other for the elements of each input. In all other respects,
the format is the same as the mechanism format for inputs
.
Function Format¶
[Function]: – the function must return an array with a number of items equal to the number of TARGET
mechanisms
for the process or system being run, each of which must match (in number and type of elements) the
target
attribute of the TARGET
mechanism for which it is
intended. This format allows targets to be constructed programmatically, in response to computations made during the
run.
Class Reference¶
-
Run.
run
(inputs, num_executions=None, reset_clock=True, initialize=False, intial_values=None, targets=None, learning=None, call_before_trial=None, call_after_trial=None, call_before_time_step=None, call_after_time_step=None, clock=CentralClock, time_scale=None)¶ Parameters: - inputs (List[input] or ndarray(input) : default default_input_value for a single execution) – the input for each execution in a sequence (see Inputs for detailed description of formatting requirements and options).
- num_executions (int : default None) – the number of executions to carry out. If it is
None
(the default), then a number of executions will be carried out equal to the number ofinputs
. Ifnum_executions
exceeds the number ofinputs
, then theinputs
will be cycled until the number of executions specified is completed. - reset_clock (bool : default True) – if
True
, resetsCentralClock
to 0 before a sequence of executions. - initialize (bool default False) – calls the
initialize
method of the system prior to a sequence of executions. - initial_values (Dict[Mechanism, List[input] or np.ndarray(input)] : default None) – the initial values assigned to mechanisms designated as
INITIALIZE_CYCLE
. - targets (List[input] or np.ndarray(input) : default None) – the target values assigned to the
MonitoringMechanism
for each execution (used for learning). The length must be equal toinputs
. - learning (bool : default None) – enables or disables learning during execution for a process or
system. If it is not specified, the current state of learning is left intact.
If it is
True
, learning is forced on; if it isFalse
, learning is forced off. - call_before_trial (Function : default=
None
) – called before eachtrial
in the sequence is executed. - call_after_trial (Function : default=
None
) – called after eachtrial
in the sequence is executed. - call_before_time_step (Function : default= ``None`) – called before each
time_step
is executed. - call_after_time_step (Function : default=
None
) – called after eachtime_step
is executed. - time_scale (TimeScale : default TimeScale.TRIAL) – specifies whether mechanisms are executed for a single time_step or a trial
- Returns –
- ------- –
- <object>.results (List[outputState.value]) – list of the values, for each execution, of the outputStates for a mechanism run directly,
or of the outputStates of the
TERMINAL
mechanisms for the process or system run


When you quit Versions, there may still be some Subversion activity in progress. This can include an update or commit action that you started, as well as automatic refreshes of the change counters in the bookmarks list.
By default, Versions waits for the graceful termination and cleanup of all background activity and server connections. This usually takes just a second or so, but may take up to a couple of minutes when there is a lot going on. You can skip this wait and force background activity to be terminated immediately by clicking the “Quit Now” button.


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{% extends "submission/base.html" %} {% load i18n %} {% block title %}{% trans "Download submission template" %}{% endblock %} {% block body_class %}{{ block.super }} download{% endblock %} {% block header %}
{% url 'core:home' as home_url %}
{% blocktrans %}
To import new datasets in Pixel, you need to
download and fill the latest version of Pixel's meta.xlsx
file. This file sums up meta-data attached to your datasets (omics area,
species, etc.), and, defines which pixels and analyses files you will want
to import.
{% endblocktrans %}
{% blocktrans %} Click the "Download" button below, and then click "Continue" to get instructions about dataset archive building and upload. {% endblocktrans %}
{% blocktrans %}
If your dataset is related to omics areas, omics units or strains that
are not referenced in the latest meta.xlsx
file, please
contact us to update our template generator.
{% endblocktrans %}


| property$ - system property information required (valid strings listed below) |
|
SystemProperty is used to 'find out' certain system-specific things that are external to the currently running program.
There are broadly two different uses for SystemProperty, one for finding out the location of certain folders on the Windows OS, and one for finding out the handles/objects being used by the Win32/DX APIs to run Blitz programs. These objects and handles can then be used via third party DLLs to add extra functionality to Blitz, although this is recommended for advanced users only. Properties - folders: systemdir - System folder windowsdir - Windows folder tempdir - Temp folder appdir - Program Files folder Properties - objects/handles: Direct3D7 Direct3DDevice7 DirectDraw7 DirectInput7 AppHWND AppHINSTANCE |
|
Print "System folder location: " + SystemProperty ("systemdir")
Print "Windows folder location: " + SystemProperty ("windowsdir") Print "Temp folder: " + SystemProperty ("tempdir") Print "Program was run from " + SystemProperty ("appdir") |


SegmentedDonation Complex Type |
ROA = Required on Add, ROU = Required on Update, Click Column Header to Sort Column |
| Name | Type | ROA | ROU | Read Only | Basic Comments | Special Comments |
| type | int | Yes | Set to 10 when journal entry is retrieved from eTapestry | |||
| accountRef | string | Yes | Must be a donor role account | |||
| accountName | string | Yes | ||||
| ref | string | Yes | Yes | |||
| final | boolean | SC1 | ||||
| date | date | Yes | ||||
| totalAmount | double | Yes | Yes | Must be greater than or equal to zero | SC2 | |
| totalNonDeductibleAmount | double | Must be greater than or equal to zero and less than or equal to the totalAmount | SC3 | |||
| segments | object array | Yes | Yes | Must have from 2 to 60 items | SC4 | |
| offsettingRef | string | Yes | SC5 |


{# Please do NOT modify this module/file because your changes may be overwritten by future installation from BRiX Editor. To customize this, please clone this module/file and work from there. #}
{{ require_css("//cdn2.hubspot.net/hubfs/346178/designmodo/ui-kit/ui-kit-header/css/ui-kit-styles.css") }}


The product is designed to connect students to one another so that they can buy and sell goods (such as furniture, electronics, apparel, etc.) directly from those in their local campus community. Students have different needs than typical members of a community, and they like to support one another. By requiring users to log in with an @rpi.edu email address, it restricts users to just RPI students, thereby creating the desired community. Users would benefit by seeing similar items and their prices, buying from fellow students as opposed to total strangers, and not having to ship items as they would if they had chosen to use other online buying/selling websites. This model could be applied to any college campus.
Students of RPI, or any university, who want to sell to fellow students and buy from fellow students.
Already, there exists a popular Facebook page for buying/selling items within the RPI Network. Facebook was not built to be a medium for buying and selling, however. The posts are not categorized by anything other than the time at which they were posted. This makes it very difficult to find what one is looking for. Craigslist is the closest in terms of functionality; however, Craigslist’s user interface is purely functional and isn’t appropriate for most transactions between students, as it is designed for whole cities. Amazon and Ebay also serve to remedy this problem but suffer from the same problem: they address an audience much larger than a campus.
Students want an easy and convenient way to buy and sell goods. They currently post their offers on Facebook, but Facebook isn’t the best application to use for this, since it was designed for the purpose of social networking, not trading. The application we wish to create would be dedicated to serving the rising demand for a better buying/selling platform.


--- layout: newsstory title: FINALLY match: Clevedon 1 Yeltz 4 ---
It's been a long time coming but the mighty Yeltz finally registered their first win, stuffing Clevedon 4-1. Two pens from Les Hines, and first goals from Darroch and Mason means we've snuck up to 20th place .
No news on the manager front as yet, but according to Halesowen News interviews will begin this week looking to appoint someone before the FA Cup game in two weeks.
Matches this week: on Tuesday we visit Stafford Rangers , and then Saturday brings rock-bottom Fisher to The Grove.
Clevedon Town: Hervin; Rollo, Haines, Peters, Coupe (Skidmore 75), Harvey, R.Ford, Badman, Mehew, Mainwaring, Lester. Subs: Llewellyn, Jeffries
Halesowen Town: Quy; Hines, Owen, Wood, Lloyd, Comyn, Collins (Cartwright 64), J. Ford, Payne (Mason 85), Griffiths (Hodgetts 81), Darroch
Booked: Owen Referee: R. Desmond (Swindon) Attendance: 303
Caretaker manager Alan Moore led Halesowen to their first win of the season at the 8th attempt with the help of two penalties strikes as Halesowen finally put their start of season nightmare behind them.
However, only 303 were there to witness it - some vague excuses about nuptuals I believe and also the Somerset shortage of fuel at a ground demanding car travel as it's in the middle of nowhere with few houses nearby. However, those that made it will now doubt annoy everyone by saying "where we you when the Yeltz won their first match of the season?". Before I forget, here's a brief stat attack - Scott Darroch's goal was the 700th for the Yeltz in ten and a bit seasons in the Southern Premier.
The visitors made an ideal start when Iain Harvey tripped Stuart Payne and Les Hines stepped up to score from the resulting penalty in the 9th minute. Jon Ford then drove just wide for the Yeltz in the 17th minute. Clevedon levelled with their first strike on goal in the 23rd minute when Danny Haines cracked home directly from a 25 yard freekick giving Andy Quy no chance. David Mehew struck the side-netting with a glancing header three minutes later from Richard Ford's freekick, but Halesowen regained the lead and control in first half stoppage time when Scott Darroch thundered an 18 yard volley into the angle of the net, taking advantage of Harvey's poor clearance. It was Darroch's first for Halesowen and a memorable strike to register the 700th goal in the Southern Premier for the Yeltz.
Clevedon were briefly excited in the 50th minute when Richard Ford had a goal disallowed for offside - the flag had been raised several seconds earlier, though the referee hadn't noticed. Jason Owen was then booked for dissent. Quy produced a fine save from Richard Ford in the 56th minute but then in a repeat of the first half, Harvey brought down Payne who had made a great right wing run and Hines again crashed home from the penalty spot to increase Halesowen's lead.
At 3-1 and the clock ticking horrid visions of Crawley briefly came back as the Yeltz conceeded midfield and gave away the ball cheaply on the edge of their box. However, the brief storm was easily weathered and with the help of substitutions Halesowen went on to record a much deserved win. Ex-Macclesfield man Michael Mason stepped off the substitute's bench to notch Halesowen's fourth in injury time after tricking his way past two defenders and curling the ball beyond Mark Hervin to make Halesowen's victory a comprehensive one.


The Gallardo was a different breed of Lamborghini, made possible by the know-how of the Volkswagen Group. The first commercial success of the automaker from Sant’Agata Bolognese had also spawned quite a number of special editions, but none is rarer than the Concept S.
Penned by Luc Donckerwolke as a barchetta with saute-vent screens instead of a traditional windshield, the Gallardo Concept S was originally limited to 100 examples. As fate would have it, only two were actually built, and this fellow here is the only one that works.
Chassis number ZHWGE32T86LA00001 has less than 200 kilometers on the odometer, was shown twice at the Pebble Beach Concours d’Elegance, and it’s estimated to fetch at least $1,300,000 at the RM Sotheby’s Abu Dhabi 2019 auction. The highest estimate is $1.6 million, a bit too much even for a hardcore car collector such as Jerry Seinfeld or Jay Leno.
The spine running between the passenger and the driver is one of the most powerful design statements of the Gallardo Concept S, which has also been aerodynamically optimized for high-speed stability. As opposed to the bone-stock model, an additional air inlet has been grafted onto the spine behind the rear headrests to channel more air into the combustion chambers of the 10-cylinder engine. The 5.0-liter blunderbuss used to be good for 520 horsepower when new, but the drivetrain is let down by the transmission.
Before the Huracan entered the scene with a dual-clutch transmission with lightning-quick gear shifts, the E-Gear served as the alternative to the gated six-speed manual. Gallardo owners know how hard it is to park the Gallardo with the electrohydraulic manual E-Gear, a tranny that also features a rather worrisome reliability record. The driveability is a headache in and of itself, jerking too much while driving in stop & go traffic.
Expected to “hold its place in Lamborghini’s modern history for years to come,” do you think that the Concept S is worth $1.3 million? 🏁


About
This is a basic Jekyll static website that is deployed using Cloud Foundry. All it takes is a simple cf push
of this site’s Github repo to get started.
If you’re interested in learning more, check out my tutorial for deploying a static site with CloudFlare SSL using Cloud Foundry.
View this site's source on Github


Use this tool to compute a hash value from a document, then copy it to the text fields of a bank transfer. This is an evidence of the existence of the content of this document at this date.
Pick or drag and drop a file to compute its hash value.


">
play/pause
stop
prev
next
undo
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file
save
𝚒
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select
cut
copy
paste
remove
cell
wrap
ﾐﾅｲﾃﾞ(／ω\)


Reviews Sentiment Analyzer is an engine that analyzes the sentiment of the reviews on a given movie name. When a search query is given it fetches the reviews live from Bing search engine, analyzes them and displays the results along with the charts representing the sentiment. When a search query is given, it classifies context based subjective reviews as positive, negative and presents them to the user, helping one to identify the sentiment of the review world at real time.
This engine is written in Python, Scikit-Learn and NLTK on top of NumPy and SciPy stack. It uses Django for webserver backend. The source code of this project is publicly available at https://github.com/ under Apache License v2.0.


Software Report of
Jays Inventory Management System
This report has all software UML diagrams for better understanding of the project developed by Jay
Faster sites deliver better business results for your clients.
- License Personal
- Full Support Yes
- Storage 100 GB
- Monthly Visitors 800k
- Templates 30
UML Diagrams
Data flow diagram
Entity Relationship diagram
Function Process: Add new Category
Application Process DFD Level : 1
Functional Process:Create new User
Functional Process:Login system
Application Process:Administrator
DFD Level 0:Context Diagram
Tabular Information
Default Users for getting into the Aplication (use the following data to login into system)
However these existing users can create new users
However these existing users can create new users
| user_id | user_name | user_password |
|---|---|---|
| 1 | Jay | root |
| 2 | admin | admin |
| 3 | guest | 123 |
Default Items present in Database (Structure of Inventory Database)
| id | product name | code | category | dealer | price |
|---|---|---|---|---|---|
| 1 | pens | 002 | Stationery | Ram | 25/- |
| 2 | spoons | 003 | Utencils | Ravi | 50/- |
| 3 | water bottles | 007 | Home needs | Raja Products | 100/- |
| 4 | Papers | 001 | Stationery | Arihant | 430/- |
| 5 | Chocolates | 006 | food | Kumar Beverages | 600/- |


|
BeyondHooperville
A Village Ecodynamics Project simulation
|
Public Member Functions | |
| xmlTradeWriter () | |
| void | recordTrade (int id, int x, int y, String network, String type, String currency, int partner, int amount) |
Records Village exchanges in a simple but readable/parseable XML format.
| com.mesaverde.filewriters.xmlTradeWriter.xmlTradeWriter | ( | ) |
Default constructor.
Initializes the XML subsystem and prepares the document & root node.
| void com.mesaverde.filewriters.xmlTradeWriter.recordTrade | ( | int | id, |
| int | x, | ||
| int | y, | ||
| String | network, | ||
| String | type, | ||
| String | currency, | ||
| int | partner, | ||
| int | amount | ||
| ) |
Records a trade.
The agent's id. The agent's X coordinate The agent's Y coordinate The network type this exchange took place in, e.g. "brn" The type of exchange this is, e.g. "payback" The currency the trade took place in, e.g "maize" The trade partner The amount of the trade
Records a trade. Finds the agent by id and adds to that record. If not found create one and add to it.


display webiste header, semantic element version of heading tags
Level 1 header
Level 2 header
this is a span
there aren't spaces between spans
added line break between spans
this is a paragraph
semantic version of paragraph tag
- first element of list
- second element of list
- first element of unordered list
- second element of unordered list
| Name |
Fave Color |
| Travis |
Blue |
| David |
Orange |
| Ryan |
Purple |
| Chris |
Red |
this is a travis tag


We assume that you will be running McErlang under the Linux operating system; we have been using Ubuntu 8.10 to develop and test the tool. Moreover we assume that Erlang is already installed on your computer and that the command erl for starting Erlang can be run directly without specifying a directory path. The model checker was developed under Erlang/OTP R12B-5; it will probably work under other releases of Erlang/OTP as well but we haven't verified this.
We recommend that you have the User Manual at hand to help you understanding this tutorial.


Defined in Module impact.sound, inherits from ig.Class
var sound = new ig.Sound( 'shoot.ogg' ); sound.play();
An instance of ig.Sound
represents a sound file, to be used as background music or game sound.
filename
The path and name of the sound file to be loaded.multiChannel
Whether multiple "channels" for concurrent play should be created. Pass false
for music or sound files that are only played once. The default is true
All your sound files have to be present in OGG Vorbis and MP3 format. Their file name has to be the same, except for the file extension. The real file name that will be loaded can be different from the given filename
, as the ig.SoundManager decides on the file extension. Also see ig.Sound.use.
E.g. for a "jump" sound, you'd have the following files:
media/sounds/jump.ogg media/sounds/jump.mp3
These can then be loaded with one of the following statements
// These 3 lines all do the same. The file extension (.ogg or .mp3) will // be decided by the SoundManager, based on the browser's capabilities // Use whatever syntax you like best. var sound = new ig.Sound( 'media/sounds/jump.ogg' ); var sound = new ig.Sound( 'media/sounds/jump.mp3' ); var sound = new ig.Sound( 'media/sounds/jump.*' );
The path and name of the image file as passed to the constructor.
The volume for this sound file from 0.0
to 1.0
. This will be multiplied with the ig.soundManager.volume before the sound is played. The default is 1
.
Plays the sound file. If the sound file was not loaded as multiChannel
(see constructor) and it is already playing, the sound restarts from the beginning, otherwise the sound is played concurrently in another channel.
Stops playback of the sound
You can disable all sounds an music by setting this property to false
before calling ig.main(). The default is true
.
When ig.Sound.enabled
is false
, no sound files will be loaded or played back. This is useful for mobile devices that currently still have vast problems with sound playback in the browser.
// Disable all sounds for mobile devices if( ig.ua.mobile ) { ig.Sound.enabled = false; } // Start the game ig.main(…)
New in 1.17
Set the number of copies to be created for each sound file. Having more copies means the sound can be played multiple times simultaneously. This has to be set before you call ig.main()
The default is 4.
New in 1.17
An object specifying the file extension and mime type for various sound formats. This is used in conjunction with ig.Sound.use. Currently implemented are:
ig.Sound.FORMAT.OGG
ig.Sound.FORMAT.MP3
ig.Sound.FORMAT.M4A
ig.Sound.FORMAT.WEBM
ig.Sound.FORMAT.CAF
New in 1.17
An array of ig.Sound.FORMAT
properties to specify the sound formats to test support for. The file extension for all sound files that will be loaded is determined by the first supported format.
The default is
[ig.Sound.FORMAT.OGG, ig.Sound.FORMAT.MP3]
I.e. if the browser supports Ogg/Vorbis, Impact will attempt to load filename.ogg
. If Ogg/Vorbis is not supported, the support for MP3 is tested and filename.mp3
will be loaded. If neither format is supported, sound is completely disabled.
Currently, Ogg/Vorbis is supported by Firefox, Opera and Chrome. MP3 and m4a are supported by Safari, Chrome and IE9.


Mount SDcard with BBB
2014-05-06
#MountSDcardwithBBB.md#
- Mounting SD card is not so easy with BBB, it is due to the problem of mysql error, saying sock file is missing and while starting satying /var folder is full
- By cheking df -h
shows it is true. So only way to increase the partition is use sd card, but noraml sd card with partiion cannot mount with BBB, due to some kernal inclination
- there is work arroun, based on [[this]](http://dev.gentoo.org/~armin76/arm/beagleboneblack/installemmc.xml) under title code listing 7.2, fromatting microsd card
- wget http://dev.gentoo.org/~armin76/arm/beaglebone/mkcard.sh
bash mkcard.sh /dev/mmcblk0
We need to pass an special environment to U-Boot, for doing so we need to create a file called uEnv.txt with the following contents on it.
Code Listing 7.3: uEnv.txt contents
bootpart=1:2 mmcroot=/dev/mmcblk1p2
Mount the first partition of the microSD card and copy this uEnv.txt file
Code Listing 7.4: Copying U-Boot and MLO to the microSD card
(Make sure you’re root) (Replace mmcblk0p1 with sdd1 if your card showed up as /dev/sdd)
mkdir /mnt/p1 ; mount /dev/mmcblk0p1 /mnt/p1
cp uEnv.txt /mnt/p1
(Unmount the partition)
umount /mnt/p1
#! /bin/sh
# mkcard.sh v0.5
# (c) Copyright 2009 Graeme Gregory<EMAIL_ADDRESS># Licensed under terms of GPLv2
#
# Parts of the procudure base on the work of Denys Dmytriyenko
# http://wiki.omap.com/index.php/MMC_Boot_Format
export LC_ALL=C
if [ -z `which bc` ]; then
echo "no bc binary found"
exit 1;
fi
if [ $# -ne 1 ]; then
echo "Usage: $0 <drive>"
exit 1;
fi
DRIVE=$1
dd if=/dev/zero of=$DRIVE bs=1024 count=1024
SIZE=`fdisk -l $DRIVE | grep Disk | grep bytes | awk '{print $5}'`
echo DISK SIZE - $SIZE bytes
CYLINDERS=`echo $SIZE/255/63/512 | bc`
echo CYLINDERS - $CYLINDERS
{
echo ,9,0x0C,*
echo ,,,-
} | sfdisk -D -H 255 -S 63 -C $CYLINDERS $DRIVE


|
DotNetOpenAuth.dll
|
the only runtime dependency for web sites to use this library. |
|
DotNetOpenAuth.pdb
|
may be useful for debugging purposes. |
|
DotNetOpenAuth.xml
|
provides Intellisense during development. |
|
DotNetOpenAuth.Contracts.dll |
Compile-time
code contracts for use by applications that reference DotNetOpenAuth.dll to
help web developers avoid mistakes and unhandled exceptions. |
|
log4net.dll
|
If present and v1.2.10, will allow for recording log messages this library emits. |
|
Microsoft.Contracts.dll
|
enables contract compile-time and runtime enforcement, but currently only used for
developing DotNetOpenAuth itself. |


{% extends "layout.html" %} {% block body %}
Welcome to Konsulttitukka Consulting Oy's video bookmarking app! Here you can browse Youtube/Twitch bookmarks or add your own bookmarks.


| DOSE: T+ 0:00 |
4.0 mg |
smoked |
2C-I |
(powder / crystals) |
| T+ 4:00 |
4.0 mg |
smoked |
2C-I |
(powder / crystals) |
I received my package on a Friday and was in a hurry to try it out. None of my friends have had any experiences with 2C-I so all I knew was from the net. I had not fasted and did not have the time for a full blown experiment that night so I decided to smoke some.
I do not have a scale so I eyed what I believe was 3-5 mg into a test tube that I torched and sucked the smoke up with a straw. The taste is a lot like freebasing. A burning plastic chemical taste. I am sure some smoke was wasted in the process but here are the results.
Initially a light foggy visual, body high and slight confusion. I thought that it would hit me hard and fast all at once but this was not the case at all. A slow progressive experience of the same as the initial. I never peaked, saw visuals nor did I have profound thoughts. I went to the movies and saw Resident Evil: Apocalypse which is a fast paced visually stimulating movie anyway so I do not think that the 2C-I made much of a difference. Within 1 hour I felt normal again without the 'Profound Enlightenment' that I had hoped for.
I returned from the movies and did the same again. 3-5 mg, in a tube smoked. This was pleasant although still nothing tripendiculas. I played on the computer, thought a bit and eventually went to bed where I had wonderful thoughts just prior to falling asleep that I only remembered in the morning.
Now this whole experience seems like a waste however, it was not. It was a nice light way to work myself into bigger and better. I relay this to those that want to try smoking it first or solo. From what I have read and what I experienced I recommend that you save your stash for a real experience and take it orally. I will relay my new experienced of oral when I do it till then I hope this helps.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


#include <OperationFactory.hpp>
◆ ~OperationConverterBase()
| virtual hsrcore::blockchain::OperationFactory::OperationConverterBase::~OperationConverterBase |
( |
| ) |
|
|
inlinevirtual |
◆ evaluate()
◆ from_variant()
| virtual void hsrcore::blockchain::OperationFactory::OperationConverterBase::from_variant |
( |
const fc::variant & |
in, |
|
|
hsrcore::blockchain::Operation & |
out |
|
) |
| |
|
pure virtual |
◆ to_variant()
| virtual void hsrcore::blockchain::OperationFactory::OperationConverterBase::to_variant |
( |
const hsrcore::blockchain::Operation & |
in, |
|
|
fc::variant & |
out |
|
) |
| |
|
pure virtual |
The documentation for this class was generated from the following file:


Setup Kubernetes Cluster
Summary
In this lab, you will create a new Kubernetes cluster, which we’ll use for the rest of the lab.
Prerequisites
-
Use either Google Cloud Shell or your terminal with Google Cloud SDK installed (the SDK comes with
kubectl
, so you do not need to install it manually) -
You already created a GCP project and set the
$PROJECT_ID
envrionement variable. -
You already cloned the directory :
https://github.com/Sfeir/kubernetes-istio-workshop
.
Run the following commands to configure gcloud
, this will save you from typing the parameters $PROJECT_ID
, zone
or region
in every gcloud
command.
gcloud config set compute/zone europe-west1-b
gcloud config set compute/region europe-west1
gcloud config set project $PROJECT_ID
|
Create the cluster
Create a cluster:
$ gcloud beta container clusters create "istio-workshop"
kubeconfig entry generated for istio-workshop.
NAME LOCATION MASTER_VERSION MASTER_IP MACHINE_TYPE NODE_VERSION NUM_NODES STATUS
istio-workshop europe-west1-b 1.13.7-gke.8 <IP_ADDRESS> n1-standard-1 1.13.7-gke.8 3 RUNNING
⏳ Wait until the cluster is created. It may takes up to 5 minues.
Connect to the cluster
Let’s check the connexion to our newly created cluster:
1- Review the cluster’s informations:
$ gcloud container clusters list
NAME LOCATION MASTER_VERSION MASTER_IP MACHINE_TYPE NODE_VERSION NUM_NODES STATUS
istio-cluster europe-west1-b 1.9.7-gke.6 <IP_ADDRESS> n1-standard-1 1.9.7-gke.6 3 RUNNING
2- Download the cluster’s credentials, they will be written to kubectl
's configuration file (~/.kube/config
`).
$ gcloud container clusters get-credentials istio-workshop
Fetching cluster endpoint and auth data.
kubeconfig entry generated for istio-workshop.
3- Check the connexion with the cluster:
$ kubectl cluster-info
Kubernetes master is running at https://<IP_ADDRESS>
GLBCDefaultBackend is running at https://<IP_ADDRESS>/api/v1/namespaces/kube-system/services/default-http-backend:http/proxy
Heapster is running at https://<IP_ADDRESS>/api/v1/namespaces/kube-system/services/heapster/proxy
KubeDNS is running at https://<IP_ADDRESS>/api/v1/namespaces/kube-system/services/kube-dns:dns/proxy
Metrics-server is running at https://<IP_ADDRESS>/api/v1/namespaces/kube-system/services/https:metrics-server:/proxy
To further debug and diagnose cluster problems, use 'kubectl cluster-info dump'.
Now you are ready to install Istio!


SeahDev
Welcome to
SeahDev's
repo!
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LimelightRed
Red glyphs for your device
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Rand - AI chooses a random direction every move. The AI will default to this if it does not recognize your input. No arguments.
Left - AI will go the left if possible, then will try going up, then move randomly if it can't do either. No arguments.
Corner/UL (both corner and ul will work as inputs) - AI will use the history of its previous moves to alternate moving up and left, and other directions (non-randomly) if it can not do those. No arguments.
Score - The AI will simulate the board after it decided each of its possible choices and choose the one that increases score the most. No arguments.
Quality - Like score, except the AI will evaluate based off a custom quality function instead of raw score. No arguments.
Lookahead - The AI will simulate depth boards into the future and try breadth random drops in each possible board in order to maximize the quality far into the future. The best performing and most patient AI.
Arguments are depth followed by breath separated by a comma such as "4, 3". Note that the number following #B in the console is the number of boards evaluated before the choice was made.
Note that increasing the arguments will cause slowdown as the AI has to analyse significantly more boards.


CP1406 - 2016 Group Project Plan
Team 33: Team Awesome
Members:
- Amos Lipsys
- Airlie Roberts
- Demosthenes Sherman
- Jordan Togo
Goals
- Increse ticket sales
- Increase sponsorship
Success Evaluation
The success of the website will be determined by measuring the improvement of ticket sales and patronage over a period of 3 months with all other promotions remaining constant. Sales will be seperated by age groups to find if our website is targeted to the correct demographic.
Target Audience
- The target audience:
- Young
- 18 - 25
- Musically Inclined
- How it will be designed to cater:
- Socal media intergation such as facebook
- Text will be broken up to make it more digestable
- Will be designed for phones and tablets
- Semi-casual/ spunky
Content
- Home (Events, News, Facebook Feed)
- Want a ticket?
- Purchase Ticket (External)
- Become a patron
- Classifed Document (Form)
- Want more Info?
- Hot off the Press
- Our Legacy
- Honorary Members
- Say Cheese
- Members only
- Contact Us
Site Flowchart
Project Links
Developed Pages


CURDLED and 2 DAYS IN THE VALLEY A film review by Michael Dequina Copyright 1996 Michael Dequina
Curdled (R) ** (out of ****) 2 days in the Valley (R) ** (out of ****) Those looking for evidence that Quentin Tarantino is the most influential filmmaker of the moment need look no further than not one, but two recent releases, the macabre comedy Curdled and the ensemble comedy-thriller 2 days in the Valley.  Tarantino himself executive produced Reb Braddock's Curdled, based on Braddock's short of the same name. The film centers on Gabriela (Angela Jones), a Miami woman who has a had a fixation on death since she was a little girl in her native Colombia--she even keeps a scrapbook of newspaper articles about murders, drawing depictions of the crimes next to each article. When she sees a TV ad recruiting people for a company that cleans up after murder scenes, she knows she's found her calling and immediately signs up. Of course, her new job puts her into contact with danger, namely one Paul Guell (William Baldwin), a.k.a. the Blue Blood Killer, who stabs and beheads women from Miami's upper social echelon. It also just so happens that Paul's murder spree is Gabriela's latest morbid obsession. The plot is enough to sustain 28 minutes, which is how long the original Curdled short lasted. Stretched out to a little over 90, the thinness of the premise is all too evident; a number of scenes and characters feel like padding, and the movie doesn't kick into full gear until the last act, when Gabriela and Paul finally meet. And for a black comedy, writers Braddock and John Maass are a little too heavy on the "black" and too light on the "comedy." While the entire package is amusing and never boring (look out for the connections to From Dusk till Dawn), real laughs are few and far-between; however, the film never skimps in terms of blood, the letting and collection of it. The wallowing in gore is more sensational and exploitative than anything else, and it is bound to turn off many a moviegoer. I suppose Braddock was trying to get under people's skin, but the subtler moments, like Gabriela's haunting retrace-the-steps-of-the-murder-victim dance, are more disturbing and funny than, say, the gruesome finale, which comes equipped with a joke that's more cheesy than hilarious. The performances are a mixed bag. Jones and her fascinating character of Gabriela (of which she played a variation in Tarantino's Pulp Fiction) are by far the best thing the film has going for it. Gabriela is an interesting paradox--a woman who always wears dresses and speaks in soft, accented, little girl tones but on the inside is far from an innocent. Baldwin, on the other hand, picks up where he left off in the awful Fair Game, relying on a single facial expression to convey certain characteristics. In Fair Game, Baldwin made his eyes bulge and gritted his teeth to convey toughness; here, he constantly makes his upper lip curl to signify evil. Mel Gorham is quite good as Gabriela's all-business cleaning partner, and MTV personality Daisy Fuentes makes her acting debut as another co-worker, but her role is so minute that it's still not clear whether she can really act or not. Strong performances are about all there is to second-time writer-director John Herzfeld's 2 days in the Valley, an interesting but none-too-funny ensemble piece. Herzfeld's knotty scenario has a murder in California's San Fernando Valley link ten diverse characters: an Olympic skier (Teri Hatcher); a no-nonsense hitman (James Spader); his icy Swedish girlfriend (Charlize Theron); a past-his-prime hitman (Danny Aiello) deathly afraid of dogs; an effete British art dealer (Greg Cruttwell); his put-upon assistant (Glenne Headly); his nurse half-sister (Marsha Mason); a has-been TV director (Paul Mazursky); and two vice cops (Eric Stoltz and Jeff Daniels). Actually, I should say _nine_ characters, for Daniels's racist cop disappears midway through the action and thus has really nothing to do with anything. Aside from his anomalous presence, Herzfeld does find interesting and creative ways to link these characters. But structure isn't everything to a picture, especially not a comedy, and 2 days's slapstick farce is more labored and silly than funny despite some high points (the vicious brawl between Hatcher and Theron being the highest). With his wide assortment of oddball characters and flashes of violence, Herzfeld was obviously shooting for a mix of Tarantino and Robert Altman--Pulp Fiction meets Short Cuts, to use the popular comparison. The film does come off as a Tarantino/Altman hybrid, but it's more like Four Rooms meets Pret-a-Porter. So just what does the mediocrity of the Tarantino-inspired Curdled and 2 days mean? Quentin, get thee back behind the camera--quick.
Michael Dequina<EMAIL_ADDRESS>Visit Mr. Brown's Movie Site at http://members.tripod.com/~MrBrown/
"It's not my fault If in God's plan He made the devil so much Stronger than a man" --Frollo (Tony Jay), "Hellfire"; Disney's The Hunchback of Notre Dame
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


I uploaded a picture of a dog doing a bad thing
That doesn't make them not a good dog.
I uploaded a cat
Cats are not good dogs. I didn't make the rules.
How does this work?
The backend uses deep learning and the cloud.
I can't figure out how to make this work on mobile
Same.
Is this a joke?
This is the most serious thing I've ever done.


We monetize content!
- syndicate bleeding-edge e-tailers
- seize ubiquitous communities
- streamline user-centric ROI
- embrace revolutionary supply-chains
- scale cross-media action-items
- monetize value-added action-items
- embrace out-of-the-box metrics
- repurpose interactive networks
- leverage frictionless methodologies
- enhance revolutionary solutions


Header padding
%
px
vw
Color picker
Text
Button
Overlay
Placements
Title Placement
Subtitle Placement
Button Placement
Image
Logo image URL
Hero image URL
Device preview
HOME
BLOG
NEWS
ABOUT
CONTACT
You request, we solve.
Lorem ipsum dolor sit amet consectetur adipisicing elit. Tenetur vitae eos unde cum voluptates?
SOLUTIONS


Hyperledger fabric- Supply chain management!
Current state
Health monitor
Profit management
Product delivery
A web-application made with Python & MaterializeCSS.


public class PostImpl extends BaseSchemaEntity implements Post
| Constructor and Description |
|---|
PostImpl() |
| Modifier and Type | Method and Description |
|---|---|
Attachment |
getAttachment()
Gets the value of the attachment property.
|
Category |
getCategory()
Gets the value of the category property.
|
Comments |
getComments()
Gets the value of the comments property.
|
java.lang.Long |
getCreationTimestamp()
Gets the value of the creationTimestamp property.
|
Creator |
getCreator()
Gets the value of the creator property.
|
java.lang.String |
getId()
Gets the value of the id property.
|
Likes |
getLikes()
Gets the value of the likes property.
|
RelationToViewer |
getRelationToViewer()
Gets the value of the relationToViewer property.
|
java.lang.String |
getSiteGroupPostUrl()
Gets the value of the siteGroupPostUrl property.
|
java.lang.String |
getSummary()
Gets the value of the summary property.
|
java.lang.String |
getTitle()
Gets the value of the title property.
|
Type |
getType()
Gets the value of the type property.
|
void |
init(org.xmlpull.v1.XmlPullParser parser)
Inits the.
|
void |
setAttachment(Attachment value)
Sets the value of the attachment property.
|
void |
setCategory(Category value)
Sets the value of the category property.
|
void |
setComments(Comments value)
Sets the value of the comments property.
|
void |
setCreationTimestamp(java.lang.Long value)
Sets the value of the creationTimestamp property.
|
void |
setCreator(Creator value)
Sets the value of the creator property.
|
void |
setId(java.lang.String value)
Sets the value of the id property.
|
void |
setLikes(Likes value)
Sets the value of the likes property.
|
void |
setRelationToViewer(RelationToViewer value)
Sets the value of the relationToViewer property.
|
void |
setSiteGroupPostUrl(java.lang.String value)
Sets the value of the siteGroupPostUrl property.
|
void |
setSummary(java.lang.String value)
Sets the value of the summary property.
|
void |
setTitle(java.lang.String value)
Sets the value of the title property.
|
void |
setType(Type value)
Sets the value of the type property.
|
void |
toXml(org.xmlpull.v1.XmlSerializer serializer)
To xml.
|
public java.lang.String getId()
Post
public void setId(java.lang.String value)
Post
public Type getType()
Post
public void setType(Type value)
Post
public Category getCategory()
Post
getCategory
in interface Post
Category
public void setCategory(Category value)
Post
setCategory
in interface Post
value
- allowed object is
Category
public java.lang.Long getCreationTimestamp()
Post
getCreationTimestamp
in interface Post
String
public void setCreationTimestamp(java.lang.Long value)
Post
setCreationTimestamp
in interface Post
value
- allowed object is
String
public Creator getCreator()
Post
getCreator
in interface Post
Creator
public void setCreator(Creator value)
Post
setCreator
in interface Post
value
- allowed object is
Creator
public java.lang.String getSummary()
Post
getSummary
in interface Post
String
public void setSummary(java.lang.String value)
Post
setSummary
in interface Post
value
- allowed object is
String
public java.lang.String getTitle()
Post
public void setTitle(java.lang.String value)
Post
public Likes getLikes()
Post
public void setLikes(Likes value)
Post
public RelationToViewer getRelationToViewer()
Post
getRelationToViewer
in interface Post
RelationToViewer
public void setRelationToViewer(RelationToViewer value)
Post
setRelationToViewer
in interface Post
value
- allowed object is
RelationToViewer
public Attachment getAttachment()
Post
getAttachment
in interface Post
Attachment
public void setAttachment(Attachment value)
Post
setAttachment
in interface Post
value
- allowed object is
Attachment
public Comments getComments()
Post
getComments
in interface Post
Comments
public void setComments(Comments value)
Post
setComments
in interface Post
value
- allowed object is
Comments
public java.lang.String getSiteGroupPostUrl()
Post
getSiteGroupPostUrl
in interface Post
String
public void setSiteGroupPostUrl(java.lang.String value)
Post
setSiteGroupPostUrl
in interface Post
value
- allowed object is
String
public void init(org.xmlpull.v1.XmlPullParser parser) throws java.io.IOException, org.xmlpull.v1.XmlPullParserException
BaseSchemaEntity
init
in class BaseSchemaEntity
parser
- the elementjava.io.IOException
- TODOorg.xmlpull.v1.XmlPullParserException
- TODOpublic void toXml(org.xmlpull.v1.XmlSerializer serializer) throws java.io.IOException
BaseSchemaEntity
toXml
in class BaseSchemaEntity
serializer
- the documentjava.io.IOException
- TODO


cmprsk: Subdistribution Analysis of Competing Risks
Estimation, testing and regression modeling of
subdistribution functions in competing risks, as described in Gray
(1988), A class of K-sample tests for comparing the cumulative
incidence of a competing risk, Ann. Stat. 16:1141-1154, and Fine JP and
Gray RJ (1999), A proportional hazards model for the subdistribution
of a competing risk, JASA, 94:496-509.
Downloads:
Reverse dependencies:
| Reverse depends: |
APtools, cr17, crrp, crrstep, crskdiag, currentSurvival, frailtyHL, gcerisk, GFGM.copula, missDeaths |
| Reverse imports: |
Epi, mafs, reportRx, riskRegression, survminer |
| Reverse suggests: |
emil, goftte, Greg, mstate, pec, RcmdrPlugin.EZR, survtmle |
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=cmprsk
to link to this page.


| 004:001 Khan | : |
O mankind! Be dutiful to your Lord, Who created you from a single person (Adam), and from him (Adam) He created his wife [Hawwa (Eve)], and from them both He created many men and women and fear Allah through Whom you demand your mutual (rights), and (do not cut the relations of) the wombs (kinship). Surely, Allah is Ever an All-Watcher over you. |
| 004:001 Maulana | : |
O people, keep your duty to your Lord, Who created you from a single being and created its mate of the same (kind), and spread from these two many men and women. And keep your duty to Allah, by Whom you demand one of another (your rights), and (to) the ties of relationship. Surely Allah is ever a Watcher over you. |
| 004:001 Pickthal | : |
O mankind! Be careful of your duty to your Lord Who created you from a single soul and from it created its mate and from them twain hath spread abroad a multitude of men and women. Be careful of your duty toward Allah in Whom ye claim (your rights) of one another, and toward the wombs (that bare you). Lo! Allah hath been a watcher over you. |
| 004:001 Rashad | : |
O people, observe your Lord; the One who created you from one being, and created from it its mate, then spread from the two many men and women. You shall regard GOD, by whom you swear, and regard the parents. GOD is watching over you. |
| 004:001 Sarwar | : |
People, have fear of your Lord who has created you from a single soul. From it He created your spouse and through them He populated the land with many men and women. Have fear of the One by whose Name you swear to settle your differences and have respect for your relatives. God certainly keeps watch over you. |
| 004:001 Shakir | : |
O people! be careful of (your duty to) your Lord, Who created you from a single being and created its mate of the same (kind) and spread from these two, many men and women; and be careful of (your duty to) Allah, by Whom you demand one of another (your rights), and (to) the ties of relationship; surely Allah ever watches over you. |
| 004:001 Sherali | : |
O ye people ! fear your Lord who created you from a single soul and of its kind created its mate, and from them twain spread many men and women; and fear ALLAH, in Whose name you appeal to one another, and fear him particularly respecting ties of kinship. Verily ALLAH watches over you. |
| 004:001 Yusufali | : |
O mankind! reverence your Guardian-Lord, who created you from a single person, created, of like nature, His mate, and from them twain scattered (like seeds) countless men and women;- reverence Allah, through whom ye demand your mutual (rights), and (reverence) the wombs (That bore you): for Allah ever watches over you. |
Concepts/Themes Covered: أركان الإسلام > التوحيد > توحيد الله تعالي > ربوبيته جلّ وعلا Pillars of Islam> Islamic> Oneness of Allah> His Divinity, the Great and Almighty أركان الإسلام > التوحيد > توحيد الله تعالي > صفات الله تعالى > الرقيب Pillars of Islam> Islamic> Oneness of Allah> His Glory's Characteristics> The All-Watcher أركان الإسلام > التوحيد > توحيد الله تعالي > وحدانيته Pillars of Islam> Islamic> Oneness of Allah> Singularity and Being Unique العمل > العمل الصالح > التقوى Action(Work)> The Good Deeds>The Piety العمل > العمل الطالح > القتل والقتال > قتل النفس التي حرّم الله Action(Work)> Bad Deeds>Killing and Fighting> Killing Anyone whose Killing Allah has Forbidden الإنسان والعلاقات الاجتماعية > الإنسان > خلقه Man and The Social Relations>Man>Being Created الإنسان والعلاقات الاجتماعية > الإنسان > شرفه ودنوه Man and The Social Relations>Man>When Honourable and When Unhonourable الإنسان والعلاقات الاجتماعية > الرجل والمرأة Man and The Social Relations>Man and Woman الإنسان والعلاقات الاجتماعية > صلة ذوي القربى Man and The Social Relations>Kinship الإنسان والعلاقات الاجتماعية > المجتمعات > خلقهم من نفس واحدة Man and The Social Relations>The Societies>Their Creation from One Self العلوم والفنون > الحقائق العلمية و الإشاة إلى وقائع أيدتهاالإكتشافات العلمية > الإنسان وخلقه Science and Art> The Scientific Facts and the Indication to Facts which have been supported by the Scientific Discoveries>Man and his Creation
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


Welcome to the College Course Catalog
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Roy Keane branded 'shameful' for criticism of Man Utd transfer target Declan Rice
Former Manchester United skipper Keane hit out at West Ham midfielder Rice for his performance in England's 4-0 win in Kosovo in a Euro 2020 qualifier on Sunday
by James Whaling, https://www.mirror.co.uk/authors/james-whaling/West Ham chief David Gold has hit back at Roy Keane for his "shameful" criticism of Hammers midfielder Declan Rice.
Rice came under fire from Keane for his display in England's 4-0 win in Kosovo on Sunday amid transfer interest from Keane's old club Manchester United .
The 20-year-old turned his back on Ireland to play for the Three Lions having represented the Boys in Green at various age group levels and winning three senior caps in friendlies.
And Gold believes that is at the heart of Keane's criticism after the former Sunderland boss labelled him "inconsistent and sloppy".
Gold told West Ham fan site Claret and Hugh: "Keane needs to get over it. Declan made his choice and I think that is what’s behind it as far as Keane is concerned.
“It’s pretty shameful when a former professional talks the way he did about a young man. It was low, outrageous, unfair and arrogant to pick him out of all the England players.
“It wasn’t even an analysis of the boy who is 20 years old who has barely started his career in the international team for God’s sake.
“This isn’t the first time he’s done it. In a previous game he said he should be sitting in the studio with the pundits rather than being on the pitch. Shocking!
"Declan has chosen to play for England and Roy Keane needs to get over that.”


In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
mnist_images.shape
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
mnist_images.shape
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
input_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels))
input_z = tf.placeholder(tf.float32, (None, z_dim))
learn_rate = tf.placeholder(tf.float32, ())
return input_real, input_z, learn_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).
def discriminator(images, reuse=False, alpha=0.2, training=True):
"""
Create the discriminator network
:param image: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
# NOTES: was 3 -> 256 -> 512 -> 1024
filter_size = 5 # use 4 or 5? was 2
def relu(x, alpha=alpha):
return tf.maximum(alpha * x, x)
with tf.variable_scope('discriminator', reuse=reuse):
# input is 28x28x3?
# Input Layer - Conv1
conv1 = tf.layers.conv2d(images, 64, filter_size, strides=(2,2), padding='same')
conv1 = relu(conv1)
# output is now 14x14x256
# Conv2
conv2 = tf.layers.conv2d(conv1, 128, filter_size, strides=(2,2), padding='same')
conv2 = tf.layers.batch_normalization(conv2, training=training)
conv2 = relu(conv2)
# output is now 7x7x512
# Conv3
conv3 = tf.layers.conv2d(conv2, 256, filter_size, strides=(2,2), padding='same')
conv3 = tf.layers.batch_normalization(conv3, training=training)
conv3 = relu(conv3)
# output is now 4x4x1024
# NOTES: had three layers in previous DCGAN
# Fully Connected Layer
fc = tf.reshape(conv3, [-1, 4*4*256])
fc = tf.layers.dense(fc, 1, activation=None)
# output is now 1
logits = fc
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
# TODO: Implement Function
# NOTES: was 1024 -> 512 -> 256 -> out_channel_dim
filter_size = 5 # use 4 or 5? (was 2)
alpha=0.2
training=is_train
reuse=not is_train
def relu(x, alpha=0.2):
return tf.maximum(alpha * x, x)
with tf.variable_scope('generator', reuse=reuse):
# First fully connected layer
fc = tf.layers.dense(z, 2*2*256, activation=None)
# Reshape to start CNN stack
fc = tf.reshape(fc, [-1, 2, 2, 256])
fc = tf.layers.batch_normalization(fc, training=training)
fc = relu(fc)
# output is now 2x2x1024
# Convt1
convt1 = tf.layers.conv2d_transpose(fc, 128, filter_size, strides=(2,2), padding='valid')
convt1 = tf.layers.batch_normalization(convt1, training=training)
convt1 = relu(convt1)
# output is now 7x7x512
# Convt2
convt2 = tf.layers.conv2d_transpose(convt1, 64, filter_size, strides=(2,2), padding='same')
convt2 = tf.layers.batch_normalization(convt2, training=training)
convt2 = relu(convt2)
# output is now 14x14x256
# Convt3
convt3 = tf.layers.conv2d_transpose(convt2, out_channel_dim, filter_size, strides=(2,2), padding='same')
# output is now 28x28xout_channel_dim
# Logits
logits = convt3
# Output Layer
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# TODO: Implement Function
smooth = 0.1 # was 0.1
g_out = generator(input_z, out_channel_dim, is_train=True)
d_real_out, d_real_logits = discriminator(input_real, reuse=False, training=True)
d_fake_out, d_fake_logits = discriminator(g_out, reuse=True, training=True)
d_real_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(\
labels=tf.ones_like(d_real_logits) * (1 - smooth), logits=d_real_logits))
d_fake_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(\
labels=tf.zeros_like(d_fake_logits), logits=d_fake_logits))
d_loss = d_fake_loss + d_real_loss
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(\
labels=tf.ones_like(d_fake_logits), logits=d_fake_logits))
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# TODO: Implement Function
t_vars = tf.trainable_variables()
g_vars = [g_var for g_var in t_vars if g_var.name.startswith('generator')]
d_vars = [d_var for d_var in t_vars if d_var.name.startswith('discriminator')]
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# TODO: Build Model
image_width = data_shape[1]
image_height = data_shape[2]
image_channels = data_shape[3]
# generate placeholders
input_real, input_z, learn_rate = model_inputs(image_width, image_height, image_channels, z_dim)
# create loss function
d_loss, g_loss = model_loss(input_real, input_z, image_channels)
# create optimization functions
d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
samples = []
losses = []
steps = 0
print_every=10
show_every=100
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
# TODO: Train Model
# rescale images from (-0.5, .5) to (-1.0, 1.0)
batch_images = batch_images * 2
steps += 1
# sample random noise for generator
batch_z = np.random.uniform(-1.0, 1.0, size=(batch_size, z_dim))
# run optimizers
_ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z})
_ = sess.run(g_train_opt, feed_dict={input_real: batch_images, input_z: batch_z})
if steps % print_every == 0:
# At the end of each epoch, get the losses and print them out
train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
train_loss_g = g_loss.eval({input_z: batch_z})
print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
# Save losses to view after training
losses.append((train_loss_d, train_loss_g))
if steps % show_every == 0:
n_images = 16
show_generator_output(sess, n_images, input_z, image_channels, data_image_mode)
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
mnist_dataset.shape
mnist_dataset.image_mode
batch_size = 128 # was 128
z_dim = 100
learning_rate = 0.001 # was 0.0002
beta1 = 0.1 # was 0.5
tf.reset_default_graph()
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
celeba_dataset.shape
celeba_dataset.image_mode
batch_size = 128
z_dim = 100
learning_rate = 0.0002
beta1 = 0.1 # was 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


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It is one of the characteristics of Mark that he uses the very Aramaic words which fell from our Lord’s lips.  It is one of the characteristics of Mark that he uses the very Aramaic words which fell from our Lord’s lips. (See 3:17; 5:41; 7:11; 14:36; 15:34.)