|
1-Wire API for .NET Version 4.00
|
Inherits com::dalsemi::onewire::container::PagedMemoryBank.
Inherited by MemoryBankAppReg, MemoryBankEEPROM, MemoryBankEEPROMblock, and MemoryBankEPROM.
Public Member Functions | |
| boolean | canRedirectPage () |
| Checks to see if this memory bank has pages that can be redirected to a new page. | |
| boolean | canLockPage () |
| Checks to see if this memory bank has pages that can be locked. | |
| boolean | canLockRedirectPage () |
| Checks to see if this memory bank has pages that can be locked from being redirected. | |
| void | lockPage (int page) throws OneWireIOException, OneWireException |
| Locks the specifed page in this memory bank. | |
| boolean | isPageLocked (int page) throws OneWireIOException, OneWireException |
| Checks to see if the specified page is locked. | |
| void | redirectPage (int page, int newPage) throws OneWireIOException, OneWireException |
| Redirects the specifed page to a new page. | |
| int | isPageRedirected (int page) throws OneWireIOException, OneWireException |
| Checks to see if the specified page is redirected. | |
| int | getRedirectedPage (int page) throws OneWireIOException, OneWireException |
| Gets the page redirection of the specified page. | |
| void | lockRedirectPage (int page) throws OneWireIOException, OneWireException |
| Locks the redirection of the specifed page. | |
| boolean | isRedirectPageLocked (int page) throws OneWireIOException, OneWireException |
| Checks to see if the specified page has redirection locked. |
One-Time-Programmable (OTP) Memory bank interface for iButtons (or 1-Wire devices) with OTP features. This interface extents the base functionality of the super-interfaces MemoryBank and PagedMemoryBank by providing One-Time-Programmable services.
The OTPMemoryBank methods can be organized into the following categories:
Read the OTP status of page 0 in the OTPMemoryBank instance 'otp':
if (otp.canRedirectPage())
{
int new_page = getRedirectedPage(0);
if (new_page != 0)
System.out.println("Page 0 is redirected to " + new_page);
}
if (otp.canLockPage())
{
if (otp.isPageLocked(0))
System.out.println("Page 0 is locked");
}
if (otp.canLockRedirectPage())
{
if (otp.isRedirectPageLocked(0))
System.out.println("Page 0 redirection is locked");
}
Lock all of the pages in the OTPMemoryBank instance 'otp':
if (otp.canLockPage())
{
// loop to lock each page
for (int pg = 0; pg < otp.getNumberPages(); pg++)
{
otp.lockPage(pg);
}
}
else
System.out.println("OTPMemoryBank does not support page locking");
| boolean canLockPage | ( | ) |
Checks to see if this memory bank has pages that can be locked.
A locked page would prevent any changes to it's contents.
true
if this memory bank has pages that can be lockedImplemented in MemoryBankAppReg, MemoryBankEEPROM, MemoryBankEEPROMblock, and MemoryBankEPROM.
| boolean canLockRedirectPage | ( | ) |
Checks to see if this memory bank has pages that can be locked from being redirected.
This would prevent a Write-Once memory from being updated.
true
if this memory bank has pages that can be locked from being redirected to a new pageImplemented in MemoryBankAppReg, MemoryBankEEPROM, MemoryBankEEPROMblock, and MemoryBankEPROM.
| boolean canRedirectPage | ( | ) |
Checks to see if this memory bank has pages that can be redirected to a new page.
This is used in Write-Once memory to provide a means to update.
true
if this memory bank pages can be redirected to a new pageImplemented in MemoryBankAppReg, MemoryBankEEPROM, MemoryBankEEPROMblock, and MemoryBankEPROM.
| int getRedirectedPage | ( | int | page | ) | throws OneWireIOException, OneWireException |
Gets the page redirection of the specified page.
Not supported by all devices.
| page | page to check for redirection |
| OneWireIOException | on a 1-Wire communication error such as no device present or a CRC read from the device is incorrect. This could be caused by a physical interruption in the 1-Wire Network due to shorts or a newly arriving 1-Wire device issuing a 'presence pulse'. |
| OneWireException | on a communication or setup error with the 1-Wire adapter. |
Implemented in MemoryBankAppReg, MemoryBankEEPROM, MemoryBankEEPROMblock, and MemoryBankEPROM.
| boolean isPageLocked | ( | int | page | ) | throws OneWireIOException, OneWireException |
Checks to see if the specified page is locked.
| page | page to check |
true
if page is locked| OneWireIOException | on a 1-Wire communication error such as no device present or a CRC read from the device is incorrect. This could be caused by a physical interruption in the 1-Wire Network due to shorts or a newly arriving 1-Wire device issuing a 'presence pulse'. |
| OneWireException | on a communication or setup error with the 1-Wire adapter. |
Implemented in MemoryBankAppReg, MemoryBankEEPROM, MemoryBankEEPROMblock, and MemoryBankEPROM.
| int isPageRedirected | ( | int | page | ) | throws OneWireIOException, OneWireException |
Checks to see if the specified page is redirected.
Not supported by all devices.
| page | page to check for redirection |
| OneWireIOException | on a 1-Wire communication error such as no device present or a CRC read from the device is incorrect. This could be caused by a physical interruption in the 1-Wire Network due to shorts or a newly arriving 1-Wire device issuing a 'presence pulse'. |
| OneWireException | on a communication or setup error with the 1-Wire adapter. |
Implemented in MemoryBankAppReg, MemoryBankEEPROM, MemoryBankEEPROMblock, and MemoryBankEPROM.
| boolean isRedirectPageLocked | ( | int | page | ) | throws OneWireIOException, OneWireException |
Checks to see if the specified page has redirection locked.
Not supported by all devices.
| page | page to check for locked redirection |
true
if redirection is locked for this page| OneWireIOException | on a 1-Wire communication error such as no device present or a CRC read from the device is incorrect. This could be caused by a physical interruption in the 1-Wire Network due to shorts or a newly arriving 1-Wire device issuing a 'presence pulse'. |
| OneWireException | on a communication or setup error with the 1-Wire adapter. |
Implemented in MemoryBankAppReg, MemoryBankEEPROM, MemoryBankEEPROMblock, and MemoryBankEPROM.
| void lockPage | ( | int | page | ) | throws OneWireIOException, OneWireException |
Locks the specifed page in this memory bank.
Not supported by all devices.
| page | number of page to lock |
| OneWireIOException | on a 1-Wire communication error such as no device present or a read back verification fails. This could be caused by a physical interruption in the 1-Wire Network due to shorts or a newly arriving 1-Wire device issuing a 'presence pulse'. |
| OneWireException | on a communication or setup error with the 1-Wire adapter. It will also be thrown if the device needs 'program' voltage and the adapter used by this device does not support it. |
Implemented in MemoryBankAppReg, MemoryBankEEPROM, MemoryBankEEPROMblock, and MemoryBankEPROM.
| void lockRedirectPage | ( | int | page | ) | throws OneWireIOException, OneWireException |
Locks the redirection of the specifed page.
Not supported by all devices.
| page | page to redirect |
| OneWireIOException | on a 1-Wire communication error such as no device present or a CRC read from the device is incorrect. This could be caused by a physical interruption in the 1-Wire Network due to shorts or a newly arriving 1-Wire device issuing a 'presence pulse'. |
| OneWireException | on a communication or setup error with the 1-Wire adapter. It will also be thrown if the device needs 'program' voltage and the adapter used by this device does not support it. |
Implemented in MemoryBankAppReg, MemoryBankEEPROM, MemoryBankEEPROMblock, and MemoryBankEPROM.
| void redirectPage | ( | int | page, |
| int | newPage | ||
| ) | throws OneWireIOException, OneWireException |
Redirects the specifed page to a new page.
Not supported by all devices.
| page | number of page to redirect |
| newPage | new page number to redirect to |
| OneWireIOException | on a 1-Wire communication error such as no device present or a CRC read from the device is incorrect. This could be caused by a physical interruption in the 1-Wire Network due to shorts or a newly arriving 1-Wire device issuing a 'presence pulse'. |
| OneWireException | on a communication or setup error with the 1-Wire adapter. It will also be thrown if the device needs 'program' voltage and the adapter used by this device does not support it. |
Implemented in MemoryBankAppReg, MemoryBankEEPROM, MemoryBankEEPROMblock, and MemoryBankEPROM.


Brian Lange is a student in the Computer Engineering program at Northwestern University. He also spends co-op quarters as a Data Scientist at Datascope Analytics, where he leads design process exercises and designs analyses, web interfaces and visualizations. Brian believes that human centered design coupled with the latest technology can make our planet more sustainable. While he's not nerding out about information density and saving the world, he enjoys science and comedy podcasts, making beer, and listening to weird music.


{% extends "layout.html" %} {% block head_declarations %} {% endblock %} {% block content %}
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stsb3.core
AddBlock
Represents the result of adding two blocks toogether.
.model(...)
is computed as x ~ p(x); y ~ p(y); x + y
Args:
left (Block)
right (Block)
_maybe_add_blocks
def _maybe_add_blocks(self, *args):
Adds parameters to prec and succ if they subclass Block.
Args:
args
: iterable of (name, parameter, bound)_maybe_remove_blocks
None
_model
None
_transform
Defines a transform from a string argument.
Currently the following string arguments are supported:
The resulting transform will be added to the transform stack iff it is not already at the top of the stack.
Args:
arg (str)
: one of the above strings corresponding to functionReturns:
self (stsb.Block)
arctanh
x -> arctanh(x)
, i.e. x -> 0.5 log ((1 + x) / (1 - x))
clear_cache
Clears the block cache.
This method does not alter the cache mode.
cos
x -> cos x
diff
x -> x[1:] - x[:-1]
Note that this lowers the time dimension from T to T - 1.
exp
x -> exp(x)
floor
x -> x - [[x]]
, where [[.]]
is the fractional part operator
invlogit
x -> 1 / (1 + exp(-x))
log
x -> log x
Block paths must be positive for valid output.
logdiff
x -> log x[1:] - log x[:-1]
Note that this lowers the time dimension from T to T - 1.
logit
x -> log(x / (1 - x))
model
def model(self, *args, **kwargs):
Draws a batch of samples from the block.
Args:
args
: optional positional argumentskwargs
: optional keyword argumentsReturns:
draws
(torch.tensor) sampled values from the blockprec
Returns the predecessor nodes of self
in the (implicit) compute graph
Returns:
_prec (list)
: list of predecessor nodes
sin
x -> sin x
softplus
x -> log(1 + exp(x))
succ
Returns the successor nodes of self
in the (implicit) compute graph
Returns:
_succ (list)
: list of successor nodes
tanh
x -> tanh(x)
, i.e. x -> (exp(x) - exp(-x)) / (exp(x) + exp(-x))
Block
Base class that all STS blocks should subclass.
Defines a number of useful modeling constructs and methods, such as deterministic transformations.
_maybe_add_blocks
def _maybe_add_blocks(self, *args):
Adds parameters to prec and succ if they subclass Block.
Args:
args
: iterable of (name, parameter, bound)_maybe_remove_blocks
None
_model
None
_transform
Defines a transform from a string argument.
Currently the following string arguments are supported:
The resulting transform will be added to the transform stack iff it is not already at the top of the stack.
Args:
arg (str)
: one of the above strings corresponding to functionReturns:
self (stsb.Block)
arctanh
x -> arctanh(x)
, i.e. x -> 0.5 log ((1 + x) / (1 - x))
clear_cache
Clears the block cache.
This method does not alter the cache mode.
cos
x -> cos x
diff
x -> x[1:] - x[:-1]
Note that this lowers the time dimension from T to T - 1.
exp
x -> exp(x)
floor
x -> x - [[x]]
, where [[.]]
is the fractional part operator
invlogit
x -> 1 / (1 + exp(-x))
log
x -> log x
Block paths must be positive for valid output.
logdiff
x -> log x[1:] - log x[:-1]
Note that this lowers the time dimension from T to T - 1.
logit
x -> log(x / (1 - x))
model
def model(self, *args, **kwargs):
Draws a batch of samples from the block.
Args:
args
: optional positional argumentskwargs
: optional keyword argumentsReturns:
draws
(torch.tensor) sampled values from the blockprec
Returns the predecessor nodes of self
in the (implicit) compute graph
Returns:
_prec (list)
: list of predecessor nodes
sin
x -> sin x
softplus
x -> log(1 + exp(x))
succ
Returns the successor nodes of self
in the (implicit) compute graph
Returns:
_succ (list)
: list of successor nodes
tanh
x -> tanh(x)
, i.e. x -> (exp(x) - exp(-x)) / (exp(x) + exp(-x))
LikelihoodCollection
def __init__(
self,
dgp,
name2lik,
name=None,
t0=None,
t1=None,
):
A NoiseBlock
subclass that facilitates running inference against multiple NoiseBlock
objects simultaneously.
Args:
dgp (callable)
: a callable that, when called, calls all NoiseBlock
objects in the correct order. This callable might be created with a call to core._collect
.name2lik (dict)
: structure {str: Block}
, name of the block mapping to the block object.See NoiseBlock
for definitions of other parameters.
_fit_autoguide
None
_maybe_add_blocks
def _maybe_add_blocks(self, *args):
Adds parameters to prec and succ if they subclass Block.
Args:
args
: iterable of (name, parameter, bound)_maybe_remove_blocks
None
_model
None
_transform
Defines a transform from a string argument.
Currently the following string arguments are supported:
The resulting transform will be added to the transform stack iff it is not already at the top of the stack.
Args:
arg (str)
: one of the above strings corresponding to functionReturns:
self (stsb.Block)
arctanh
x -> arctanh(x)
, i.e. x -> 0.5 log ((1 + x) / (1 - x))
clear_cache
Clears the block cache.
This method does not alter the cache mode.
cos
x -> cos x
diff
x -> x[1:] - x[:-1]
Note that this lowers the time dimension from T to T - 1.
exp
x -> exp(x)
fit
def fit(self, method="nf_block_ar", method_kwargs=dict(), verbosity=0.01):
Fits a guide (variational posterior) to the model.
Wraps multiple Pyro implementations of variational inference. To minimize noise in the estimation you should follow the Pyro guidelines about marginalizing out discrete latent rvs, etc.
Args:
method (str)
: one of “advi”, “low_rank”, or “nf_block_ar”.
"advi"
: fits a diagonal normal distribution in unconstrained latent space"low_rank"
: fits a low-rank multivariate normal in unconstrained latent space. Unlike the diagonal normal, this guide can capture some nonlocal dependence in latent rvs."nf_block_ar"
: fits a normalizing flow block autoregressive neural density estimator in unconstrained latent space. This method uses two stacked block autoregressive NNs. See the Pyro docs for more details about this.method_kwargs (dict)
: optional keyword arguments to pass to Pyro’s inference capabilities. If no keyword arguments are specified, sane defaults will be passed instead. Some arguments could include:
"niter"
: number of iterations to run optimization (default 1000
)"lr"
: the learning rate (default 0.01
)"loss"
: the loss function to use (default "Trace_ELBO"
)"optim"
: the optimizer to use (default "AdamW"
)verbosity (float)
: status messages are printed every int(1.0 / verbosity)
iterationsfloor
x -> x - [[x]]
, where [[.]]
is the fractional part operator
invlogit
x -> 1 / (1 + exp(-x))
log
x -> log x
Block paths must be positive for valid output.
logdiff
x -> log x[1:] - log x[:-1]
Note that this lowers the time dimension from T to T - 1.
logit
x -> log(x / (1 - x))
model
def model(self, *args, **kwargs):
Draws a batch of samples from the block.
Args:
args
: optional positional argumentskwargs
: optional keyword argumentsReturns:
draws
(torch.tensor) sampled values from the blockposterior_predictive
def posterior_predictive(
self,
nsamples=1,
):
Draws from the posterior predictive distribution of the graph with self
as the root
Args:
nsamples (int)
: number of samples to drawReturns:
samples (torch.tensor)
prec
Returns the predecessor nodes of self
in the (implicit) compute graph
Returns:
_prec (list)
: list of predecessor nodes
prior_predictive
def prior_predictive(
self,
nsamples=1,
):
Draws from the prior predictive distribution of the graph with self
as the root
Args:
nsamples (int)
: number of samples to drawReturns:
samples (torch.tensor)
sample
def sample(
self,
nsamples=100,
thin=0.1,
burnin=500,
):
Sample from the model’s posterior using the Pyro implementation of the No-U Turn Sampler
This could take a very long time for long time series. It is recommended to use .fit(...)
instead.
Args:
nsamples (int)
: number of desired samples after burn in and thinningthin (float)
: every int(1.0 / thin)
sample is keptburnin (int)
: samples[burnin:]
are keptsin
x -> sin x
softplus
x -> log(1 + exp(x))
succ
Returns the successor nodes of self
in the (implicit) compute graph
Returns:
_succ (list)
: list of successor nodes
tanh
x -> tanh(x)
, i.e. x -> (exp(x) - exp(-x)) / (exp(x) + exp(-x))
MultiplyBlock
Represents the result of multiplying two blocks toogether.
.model(...)
is computed as x ~ p(x); y ~ p(y); x * y
Args:
left (Block)
right (Block)
_maybe_add_blocks
def _maybe_add_blocks(self, *args):
Adds parameters to prec and succ if they subclass Block.
Args:
args
: iterable of (name, parameter, bound)_maybe_remove_blocks
None
_model
None
_transform
Defines a transform from a string argument.
Currently the following string arguments are supported:
The resulting transform will be added to the transform stack iff it is not already at the top of the stack.
Args:
arg (str)
: one of the above strings corresponding to functionReturns:
self (stsb.Block)
arctanh
x -> arctanh(x)
, i.e. x -> 0.5 log ((1 + x) / (1 - x))
clear_cache
Clears the block cache.
This method does not alter the cache mode.
cos
x -> cos x
diff
x -> x[1:] - x[:-1]
Note that this lowers the time dimension from T to T - 1.
exp
x -> exp(x)
floor
x -> x - [[x]]
, where [[.]]
is the fractional part operator
invlogit
x -> 1 / (1 + exp(-x))
log
x -> log x
Block paths must be positive for valid output.
logdiff
x -> log x[1:] - log x[:-1]
Note that this lowers the time dimension from T to T - 1.
logit
x -> log(x / (1 - x))
model
def model(self, *args, **kwargs):
Draws a batch of samples from the block.
Args:
args
: optional positional argumentskwargs
: optional keyword argumentsReturns:
draws
(torch.tensor) sampled values from the blockprec
Returns the predecessor nodes of self
in the (implicit) compute graph
Returns:
_prec (list)
: list of predecessor nodes
sin
x -> sin x
softplus
x -> log(1 + exp(x))
succ
Returns the successor nodes of self
in the (implicit) compute graph
Returns:
_succ (list)
: list of successor nodes
tanh
x -> tanh(x)
, i.e. x -> (exp(x) - exp(-x)) / (exp(x) + exp(-x))
NoiseBlock
Base class for all likelihood function-type blocks
Implements a number of inference wrappers to Pyro implementations.
Args:
dgp (Block)
: the latent data-generating process for which self
serves as a likelihood functiondata (None || torch.tensor)
: the observed data. If data is None
, then using the noise block is equivalent to drawing from the prior of a state space modelname (None || str)
: a unique name of the block. If name is None
, a unique name will be automatically generatedFor other argument documentation, see Block
_fit_autoguide
None
_maybe_add_blocks
def _maybe_add_blocks(self, *args):
Adds parameters to prec and succ if they subclass Block.
Args:
args
: iterable of (name, parameter, bound)_maybe_remove_blocks
None
_model
None
_transform
Defines a transform from a string argument.
Currently the following string arguments are supported:
The resulting transform will be added to the transform stack iff it is not already at the top of the stack.
Args:
arg (str)
: one of the above strings corresponding to functionReturns:
self (stsb.Block)
arctanh
x -> arctanh(x)
, i.e. x -> 0.5 log ((1 + x) / (1 - x))
clear_cache
Clears the block cache.
This method does not alter the cache mode.
cos
x -> cos x
diff
x -> x[1:] - x[:-1]
Note that this lowers the time dimension from T to T - 1.
exp
x -> exp(x)
fit
def fit(self, method="nf_block_ar", method_kwargs=dict(), verbosity=0.01):
Fits a guide (variational posterior) to the model.
Wraps multiple Pyro implementations of variational inference. To minimize noise in the estimation you should follow the Pyro guidelines about marginalizing out discrete latent rvs, etc.
Args:
method (str)
: one of “advi”, “low_rank”, or “nf_block_ar”.
"advi"
: fits a diagonal normal distribution in unconstrained latent space"low_rank"
: fits a low-rank multivariate normal in unconstrained latent space. Unlike the diagonal normal, this guide can capture some nonlocal dependence in latent rvs."nf_block_ar"
: fits a normalizing flow block autoregressive neural density estimator in unconstrained latent space. This method uses two stacked block autoregressive NNs. See the Pyro docs for more details about this.method_kwargs (dict)
: optional keyword arguments to pass to Pyro’s inference capabilities. If no keyword arguments are specified, sane defaults will be passed instead. Some arguments could include:
"niter"
: number of iterations to run optimization (default 1000
)"lr"
: the learning rate (default 0.01
)"loss"
: the loss function to use (default "Trace_ELBO"
)"optim"
: the optimizer to use (default "AdamW"
)verbosity (float)
: status messages are printed every int(1.0 / verbosity)
iterationsfloor
x -> x - [[x]]
, where [[.]]
is the fractional part operator
invlogit
x -> 1 / (1 + exp(-x))
log
x -> log x
Block paths must be positive for valid output.
logdiff
x -> log x[1:] - log x[:-1]
Note that this lowers the time dimension from T to T - 1.
logit
x -> log(x / (1 - x))
model
def model(self, *args, **kwargs):
Draws a batch of samples from the block.
Args:
args
: optional positional argumentskwargs
: optional keyword argumentsReturns:
draws
(torch.tensor) sampled values from the blockposterior_predictive
def posterior_predictive(
self,
nsamples=1,
):
Draws from the posterior predictive distribution of the graph with self
as the root
Args:
nsamples (int)
: number of samples to drawReturns:
samples (torch.tensor)
prec
Returns the predecessor nodes of self
in the (implicit) compute graph
Returns:
_prec (list)
: list of predecessor nodes
prior_predictive
def prior_predictive(
self,
nsamples=1,
):
Draws from the prior predictive distribution of the graph with self
as the root
Args:
nsamples (int)
: number of samples to drawReturns:
samples (torch.tensor)
sample
def sample(
self,
nsamples=100,
thin=0.1,
burnin=500,
):
Sample from the model’s posterior using the Pyro implementation of the No-U Turn Sampler
This could take a very long time for long time series. It is recommended to use .fit(...)
instead.
Args:
nsamples (int)
: number of desired samples after burn in and thinningthin (float)
: every int(1.0 / thin)
sample is keptburnin (int)
: samples[burnin:]
are keptsin
x -> sin x
softplus
x -> log(1 + exp(x))
succ
Returns the successor nodes of self
in the (implicit) compute graph
Returns:
_succ (list)
: list of successor nodes
tanh
x -> tanh(x)
, i.e. x -> (exp(x) - exp(-x)) / (exp(x) + exp(-x))
_add_fns_to_repr
None
_apply_fns
None
_closure_init
def closure_init(init_fn):
Generates a complete constructor given only the implementation-specific portion
_collect
Generates a single callable model from a collection of NoiseBlock
instances.

Args:
*liks
: each element of liks
must subclass NoiseBlock
name (str)
: the name of the returned LikelihoodCollection
.construct_init
def construct_init(fn_addr_param):
Constructs an implementation-specific constructor given a parameter specification dictionary.
Args:
fn_addr_params (dict)
: {function_address: parameters}
. parametersis a
dict` with structure
{
"expand": bool,
"domain": domain defined in constants,
"default": Block || torch.tensor || pyro.distributions
}
forecast
def forecast(dgp, samples, *args, Nt=1, nsamples=1, **kwargs):
Forecasts the root node of the DGP forward in time.
Args:
dgp (Block)
: the root node to forecast forwardsamples (dict)
: {semantic site name: value}
The value tensors should have shape (m, n, T)
, where m
is the number of samples, n
is the batch size, and T
is the length of the time series*args
: any additional positional arguments to pass to dgp.model
Nt (int):
number of timesteps for which to generate forecast. Forecast is generated from t1 + 1
to t1 + 1 + Nt
.nsamples (int)
: number of samples to draw from the forecast distributiondesign_tensors (Dict[str, torch.Tensor])
:**kwargs
: any additional keyword arguments to pass to dgp.model
name_to_definite
def name_to_definite(skeleton, *names,):
Makes the names associated with the block skeleton into torch.tensor
s according to the skeleton’s current interpretation.
Args:
skeleton (Block-like)
: an incompletely-constructed block. The block will be incompletely constructed because the definition of this method is part of the block’s definition.*names (List[str])
: names to make definiteReturns:
tuple
of torch.tensor
corresponding to the passed names.redefine
def redefine(
block,
attribute,
obj,
):
Redefines an attribute of a block to the passed object
Args:
block (Block)
attribute (str)
obj (Block || torch.tensor || pyro.distributions)
register_address_component
def register_address_component(
name,
expand,
domain=None,
):
Registers a new functional address component and metadata.
Addresses in stsb3
look like x/y-z
or y-z
, where x
is a context coomponent, y
is the name of the rv, and z
describes its function. register_address_component
allows for runtime definition of z
address components for use in new blocks or structure search algorithms.
Args:
name (str)
: the name. E.g., already-defined names include loc
, scale
, and amplitude
expand (bool)
: whether objects whose address contains this component can be expanded using Block
sdomain (None || tuple)
: if expand
, must be a tuple in constants.DOMAINS
(i.e., one of (-inf, inf)
, (0, inf)
, or (0, 1)
)This function is not safe – if name
already exists in constants
, this will overwrite its definition and properties.


Browse to JSON:
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Copyright © 2019 —
Faulon's group


#!/usr/bin/python3
# Copyright (c) 2016-2017, henry232323
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
"""A basic subclass of asyncio.Protocol using a buffer (for combined / split
irc messages, calls self.client.x for all callback events"""
import asyncio
[docs]class ClientProtocol(asyncio.Protocol):
"""Default protocol, forewards all events to the client"""
def __init__(self, client):
self.client = client
self.buffer = bytes()
self.sockname = None
self.transport = None
[docs] def connection_made(self, transport):
"""Called on a successful connection, calls client.connection_made"""
self.sockname = transport.get_extra_info("sockname")
self.transport = transport
asyncio.ensure_future(self.client.connection_made())
[docs] def connection_lost(self, exc):
"""Called on a lost connection, calls client.connection_lost"""
asyncio.ensure_future(self.client.connection_lost(exc))
[docs] def data_received(self, data):
"""Called when data is received, calls client.data_received"""
self.buffer += data
pts = self.buffer.split(b"\n")
self.buffer = pts.pop()
for el in pts:
asyncio.ensure_future(self.client.data_received(el))
[docs] async def send(self, message):
"""Send an unencoded message to the server, will be encoded"""
self.transport.write(message.encode())
[docs] async def send_raw(self, data):
"""Send raw bytes to the server"""
self.transport.write(data)


How do I register for a CBD account?
Any person can have a CBD account which will give you access to register information in all CBD clearing-houses including the Biosafety Clearing-House (BCH), the ABS Clearing-House (ABS-CH) and the clearing-house mechanism (CHM) under the Convention. You can register for a CBD account at accounts.cbd.int/signin
Make sure you enter all the information required as instructed on the form. You’ll automatically receive an e-mail with a link you have to click to confirm your account.
I am not sure if my account is still active?
The easiest way to check if you are registered accounts.cbd.int is to go to the forgot password page and enter the email associated with that account. If you have an account you will be emailed by the system a link to re-activate your password.
Do I need a CBD account for searching information in the ABS-CH?
No. You can search information and consult the country profiles without requiring a CBD account.
What kind of information can I register in the ABS-CH having a CBD account?
The type of information you can register will depend on your role in the ABS-CH. National records can only be registered by national authorized users (NAUs) or publishing authority (PA) duly designated by a country.
However, any registered user will be able to register Reference Records in the Virtual Library.
Forgot your password?
You can click here accounts.cbd.int/password/reset. You will be asked for your email address and, if you are a registered user, an email will be sent immediately to your registered email account with instructions to reset your password.
Problems signing in? Check your internet browser.
At the moment, the ABS-CH works best with the most recent verisons of internet browsers (Firefox, Chrome or Internet Explorer), and sometimes problems arise from having older versions of the browsers. We will continue working to improve browser compatibility, but in the meanwhile, please make sure you have the most recent version of your favorite browser installed on your computer:
- For Firefox: http://www.mozilla.org/en-US/firefox/new /
- For Chrome: https://www.google.com/intl/en/chrome/browser/ei
- For Internet explorer: http://windows.microsoft.com/en-ca/internet-explorer/download-ie
How do I update my profile?
You can update your profile here: accounts.cbd.int/profile. You will be asked to sign-in before you can update your profile information.
Please note, to set up your account as a Publishing Authority (PA) or National Authorized User (NAU) will need to be offically deisgnated by the ABS or National Focal Point.


Add Store Location
Name of Location:
Max Customers per Hour:
Min Customers per Hour:
Avg Cookies per Sale:
Create a new store
Daily Salmon Cookie Sales by Hour and Location


:: com :: sun :: star :: bridge ::
|
| interface XProtocolProperties |
|
- Description
- Bridge internal interface, that allows to change protocol settings of the remote
counter part.
In general, this interface is implemented by the bridge itself. It must not
be called from outside the bridge.
INTERNAL INTERFACE, DO NOT USE IT ELSEWHERE!
|
| Methods' Summary |
| getProperties |
called to get a list of bridge internal properties. Which properties can be retrieved,
is protocol dependent.
|
| requestChange |
called to initiate a protocol change.
|
| commitChange |
called to commit a protocol change.
|
| Methods' Details |
getProperties
- Description
- called to get a list of bridge internal properties. Which properties can be retrieved,
is protocol dependent.
The properties MUST NOT change between a requestChange and a commit change call.
|
|
requestChange
| long |
| requestChange( |
[in] long |
nRandomNumber ); |
- Description
- called to initiate a protocol change.
This method should always be called in the scope of the local bridge setting object,
because the remote counter part may do such a call at the same time
(typically at startup time).
- Parameter nRandomNumber
- In case both processes call requestChange at the same time,
the caller with the higher nRandomNumber is allowed to call
commitChange.
- Returns
- 1, if the caller may ( and MUST !!!) call commitChange.
0, if the caller is not allowed to call commitChange. This can only happen,
if the other bridge has called requestChange at the same time and
nRandomNumber is smaller than the other bridge's nRandomNumber.
The remote counterpart is supposed to call commitChange within a small
time span. Please call requestChange() after the remote counterpart has
called commitChange().
-1 if the nRandomNumber is of the same value as the previously
sent requestChange (sent by the remote counterpart). This is a draw :o).
Generate a new random number and try again.
|
|
commitChange
- Description
- called to commit a protocol change.
It is only allowed to call commitChange, if requestChange
has been called previously and the return value was true. The new properties are
valid after the reply of commitChange has been received.
Note, that this is difficult for the callee, because it must marshal the reply
with the old settings.
All properties not mentioned in the list are unchanged.
Note that the bridge must be blocked for other threads,
before commitChange is sent and unblocked
after the reply has been received. This blocks the bridge.
- Throws
- InvalidProtocolChangeException
when the remote counterpart could not change at least one of the properties.
No property has been changed. requestChange must be called
again to initiate a new change of the protocol.
|
|
Top of Page
Copyright © 2013, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


Soon the freeCodeCamp curriculum will be 100% project-driven learning. Instead of a series of coding challenges, you'll learn through building projects - step by step. Before we get into the details, let me emphasize: we are not changing the certifications. All 6 certifications will still have the same 5 required projects. We are only changing the optional coding challenges.
After years - years - of pondering these two problems and how to solve them, I slipped, hit my head on the sink, and when I came to I had a revelation! A vision! A picture in my head! A picture of this! This is what makes time travel possible: the flux capacitor!
It wasn't as dramatic as Doc's revelation in Back to the Future. It
just occurred to me while I was going for a run. The revelation: the entire curriculum should be a series of projects. Instead of individual coding challenges, we'll just have projects, each with their own seamless series of tests. Each test gives you just enough information to figure out how to get it to pass. (And you can view hints if that isn't enough.)
The entire curriculum should be a series of projects
No more walls of explanatory text. No more walls of tests. Just one
test at a time, as you build up a working project. Over the course of passing thousands of tests, you build up projects and your own understanding of coding fundamentals. There is no transition between lessons and projects, because the lessons themselves are baked into projects. And there's plenty of repetition to help you retain everything because - hey - building projects in real life has plenty of repetition.
The main design challenge is taking what is currently paragraphs of explanation and instructions and packing them into a single test description text. Each project will involve dozens of tests like this. People will be coding the entire time, rather than switching back and forth from "reading mode" to "coding mode".
Instead of a series of coding challenges, people will be in their code editor passing one test after another, quickly building up a project. People will get into a real flow state, similar to what they experience when they build the required projects at the end of each certification. They'll get that sense of forward progress right from the beginning. And freeCodeCamp will be a much smoother experience.


--- layout: post title: 'FunkyZoom : techniques for browsing photos on mobiles (via Anti-mega)' date: 2004-09-15 00:00:00 category: links ---


Fix the metadata so they agree with each other and re-load the data.
If you want to keep them as separate samples, change the sample label for one (so it will have a new sampleID generated) and re-upload.


This website generates a graph of a state's (United States) COVID-19 cases and deaths.
Credit to http.cat for the great 404, 418, and 500 photos.


Download
There are three ways to use Kraken on your project:
- Download Kraken directly from GitHub.
- Clone Kraken from GitHub:
git@github.com:cferdinandi/kraken.git
-
Install Kraken using your favorite package manager:
- NPM:
npm install cferdinandi/kraken
- Bower:
bower install https://github.com/cferdinandi/kraken.git
- Component:
component install cferdinandi/kraken


{%extends "blog/base.html" %} {% block content %} {% load staticfiles %}
Once you have logged out the main page will as shown above.ALl featues of the Librolet will not be available for use.User can view other posts only and related data will be available only after user login.


I'm pursuing my Master's degree in Computer Engineering with special focus on Machine Learning and Software Development. I have previous research experience in Image Processing and Computer Vision. Currently, I'm looking to develop industry relevant skills.
Developed a “Facial Expression Recognition and Classification” algorithm that recognised and classified six facial expressions using frontal face images of varied subjects. Efficiency of 86% achieved.
Developed a system that collected data in real-time on parameters, such as, temperature, humidity and wind speed from various areas of the city and relayed it to the user's mobile for further analysis.
Efficient Recognition and Classification System based on Morphological Processing of Frontal Face Images: It was published at the 10th International Conference on Industrial and Information Systems (ICIIS) held at University of Peradeniya, Sri Lanka.
Publication URLStatistical and Theoretical Analysis of MRI and fMRI Data of Confirmed Alzheimer's Disease Cases : Using hippocampal atrophy as the prime indicator, an algorithm that could analyze MRI and fMRI images of patients with mild cognitive impairment (MCI) to accurately diagnose as positive or negative for early AD, was developed.
GPA: NA
GPA: 3.30
Outside of academics, I enjoy traveling, sports, public speaking and reading.
Particularly, I'd like to pursue public speaking and tennis more actively.
Daily recreational sources are music and television shows (FRIENDS to be precise).


The maximum height of this paragraph is set to 50px. The maximum height of this paragraph is set to 50px. The maximum height of this paragraph is set to 50px. The maximum height of this paragraph is set to 50px. The maximum height of this paragraph is set to 50px. The maximum height of this paragraph is set to 50px. The maximum height of this paragraph is set to 50px. The maximum height of this paragraph is set to 50px. The maximum height of this paragraph is set to 50px. The maximum height of this paragraph is set to 50px.


Frequently Asked Questions
What is this?
This is the Decompiler Explorer! It is an interactive online decompiler which shows equivalent C-like output of decompiled programs from many popular decompilers. It's meant to be the reverse of the amazing Compiler Explorer.
What is a decompiler?
A decompiler does the opposite of a compiler! It takes binaries and turns them back into source code (with varying degreees of success depending on the compiler, compiler settings, language, architecture, complexity, and many other factors).
Who made it?
Vector 35, the developers of Binary Ninja (one of the decompilers used on the site), created an internal original prototype which eventually became this thanks to input from the community and some additional inspiration from tools like mdec. Decompiler Explorer is now a fully open source project available on GitHub.
The current iteration is meant to be a community maintained tool and as such, contributions are welcome. More details are available on an introductory blog post.
What is in it?
The following decompilers are currently a part of the Decompiler Explorer:
What happens to the binaries?
All submitted binaries are saved and made available to any of the authors of the tools used so they may improve their decompilers. If you're such an author who would like access, let us know!.
Can I run my own version of this site?
Not publicly with all tools. You may not run your own public version and include any of the commercial tools. This public service is being run with the express permission of the commercial entities involved and they do not intend for other public versions of this website to be run as they are not compatible with the relevent EULAs/licenses.
Note that private instances may or may not be compatible depending on the type of license you have. The safest thing to do is to check with the vendors in question if you wish to run an internal service to verify your services complies with the terms of the license, or only use the open-source services.
In particular, third-parties running their own copy will run afoul of the specific license limitations on time-sharing services and derivative works in Binary Ninja and IDA Pro respectively.
Vector 35 has custom "headless" licenses for Binary Ninja that may be used for longer-term internal deployments (contact them for more info).
Where is the code?
The Decompiler Explorer is completely open source and can be found at GitHub.
How is it being hosted?
Vector 35 and Hex-Rays jointly sponsor the hosting on Digital Ocean as a community service.
Specifically, the infrastructure includes:
- 1 x "basic" dual-core, 4GB RAM web-front-end -- note that "basic" doesn't specify the exact CPU specs
- 4 x "basic" quad-core, 8GB RAM worker instances (sometimes scales temporarily to 8 depending on load)
How can I contribute?
Please submit a pull request to the main GitHub repository. Thanks!


OK Google, Pronounce YHWH
You're the organizer of a leading conference in computer science, the H-sorted Copyleft Hallux Symposium. Anyway, you've written an email to send out to your fellow scientists to ask them to submit.  Will you be able to recover your data?
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Animal Crossing: New Horizons
It's Out Now!
Get it from
Nintendo
or
Amazon
For More Info
Reddit
Nintendo
Nintendo Switch
Animal Crossing
Twitter
Nintendo
Animal Crossing


Please wait.. Trying to fetching all now-playing-movies data... It only takes time for the first time...


Welcome to ITECH 270
Chapter 5 - Advanced CSS
Chapter 6 - Basic JavaScript
Chapter 7 - JS in the Browser
Chapter 9 - jQuery


My previous blog post offered a high-level glance into the loan descriptions at kiva.org. For my final project at the Metis Data Science Bootcamp, I delved a bit deeper in the micro-finance mechanics at Kiva, looking for a practical problem to solve. Earlier research papers on Kiva have studied for example how images of Kiva borrowers influence loan decisions by lenders [1], or which factors motivate lenders to make additional loans [2]. My high-level aim in this project was double: produce new insights, and, above all, build something ready to use and demo — all in the course of four weeks.
Let's start by setting the stage.
The abovementioned paper [2] contains a clear overview of the Kiva loan process. It is repeated here below, but with a couple of additions (two ovals and one curved arrow) to point out some particulars that drew my attention:
The crucial point, of course, is whether enough lenders will be found within a loan request's expiration time to back-fill the loan. When a loan is not completely funded in time, the allocated amounts return to the Kiva lenders, and the full risk stays with the field partner. The predisbursed loan itself, however, remains: the borrower already received the money, and is expected to pay it back over time.
Getting loans fully funded is in the interest of multiple parties:
Efficient loan funding is therefore beneficial to the Kiva ecosystem as a whole. This is especially true in times when the total amount of loan requests exceeds the available money in the system.
By the way, Kiva loans being interest free, there is no other built-in mechanism for steering offer and demand, than trust, benevolence and advertizing — whether by word of mouth or otherwise.
In terms of growth, Kiva has been a phenomenal success. Since its start in 2005, the number of loan requests has risen year after year. In the early years, there seems to have been enough available money in the system to fully backfill most, if not all loan requests. However, since 2012, a significant number of predisbursed loans do not get backfilled, as is illustrated below.
The rising number of non-backfilled loans since 2012 may be due to the relative lack of fresh inflowing capital from Kiva lenders, and/or to an intrinsic deterioration of loan quality. Whichever the reason, it is unknown if, to what extent and how soon the rising proportion of non-backfilled loans has translated into a growing reticence for field partners to predisburse loans in the first place: no data are currently available to test either of these hypotheses. But from the field partners' perspective, there must be an inherent limit to increase their own capital and incur more risk. In a system with acceptable risk-taking, any transgression of this limit must eventually hamper the (invisible) predisbursal rate of initial loan requests, including valid ones.
Especially male borrowers are lagging behind. Not only have they historically entered the Kiva system less frequently than females; but since 2012, loans to male borrowers have proportionally had a harder time getting backfilled than loans predisbursed to females.
Irrespective of the amount of money present in the Kiva ecosystem at any moment, we should at least try to make the available funds flow as efficiently as possible.
As a final introductory note, let's visualize how (much) money flows through the global Kiva ecosystem. The interactive Sankey diagram below summarizes, for the period 2012 through 2014, the money flows between lenders and borrowers. In these three years, more than 321 million dollars have been lent — and then (hopefully) repaid.
The connections in the center "column" show the country-to-country money flows. The left and right column do not really represent flows; they rather group the countries by region, respectively from the lenders' and the borrowers' perspective.
To see the dollar amounts, simply hover with the mouse over the graph. After a second or two, a popup will appear.
All figures are calculated from a Kiva JSON data snapshot downloaded on February 17, 2015. The software code is available on GitHub.
Now that the stage is set, let's define our problem more precisely.
The business goal in this project is to reduce the amount of non-productive, sleeping money in the system.
The main observation is that it is better for X% of loans to be 100% funded, than for 100% of loans to be funded at X% each. In the latter (granted, somewhat extreme) case, following Kiva policy, all funds would simply return to the lenders. This means that not a single loan would be backfilled!
To reach this business goal, I propose a tool that provides an a priori insight into the chance of a loan request being fully funded by available Kiva lender money. Next to that, this study aims to uncover actionable insights to the concerned parties, who may benefit as follows:
Disclaimer: I am fully aware that each of these uses are possibly controversial. They might indeed carry unintended side-effects, such as information asymmetry, perceived or real favoritism, and, in the worst case, intentional misrepresentation of loan characteristics. Since trust and transparency are key values for Kiva — or any other finance system, for that matter — any potential deployment into a global production setting should only happen after careful analysis of the risks and benefits involved. In the framework of this 4-week final project, I make no further statements about this important question. By the way, and for the record: Kiva.org was not informed about this project before it was completed and published here.
My technical solution consists of 4 main components:
I now elaborate on each component, following the steps (1-6) in which they come into play. This description is somewhat idealistic, in that the real-life R&D process was more of an agile and iterative nature.
All code is available on GitHub. Steps 1 and 2 were run from an iPython notebook.
| Technology | Used for | Useful for |
|---|---|---|
| MongoDB | Local storage of documents/objects in the Kiva JSON snapshot | Selection of loans from specific years |
| langid.py | Language identification of Kiva loan descriptions | Correcting erroneous language identification by Kiva, due to mixed original versions and their translations |
| Pandas and numpy | Efficient storage of (mostly numeric) data points | Training and test data for supervised learning |
| Matplotlib | Plotting data | Producing the loan bar chart, split per gender and quarter (see above), and feature polarity graphs (see below) |
| gensim | Conversion of loan descriptions to a bag-of-words representation | Formatting these data in Blei Corpus Format, readable by SLDA (see below) |
| SLDA (Supervised Latent Dirichlet Allocation) | Training and evaluation of an auxiliary topic model | Reduction of 1000-dimensional bag-of-words vectors from the loan descriptions to 20 features (distribution over topics) |
In preparation of a previous project, I had already downloaded a Kiva snapshot of approximately 775.000 loans and their respective lenders on February 17, 2015. The data are reused in this project.
| Technology | Used for | Useful for |
|---|---|---|
| Scikit-learn | Training and evaluating a logistic regression model | Producing predictions and (rankable) prediction scores between 0-1 |
| pickle | Serialization of the trained model | Deployment to the live web app |
Three types of input features were extracted and/or derived from the JSON snapshot:
The to-be-predicted label is defined as follows: divide the actual "funded_amount" by the requested "loan_amount". If this ratio is greater than or equal to 1, the loan is fully funded (label 1). If not, the loan is not fully funded (label 0).
The data set consists of all usable loans from the years 2012 through 2014: 21 K negative and 420 K positive instances. This a priori 20/1 ratio calls for careful class weight settings during model training.
From the outset, it was my plan to fit a logistic regression model against the training data, using 10-fold cross validation. The chosen class weight setting is 'auto', which should compensate for the strong predicted label imbalance in the training set. I also included some experimentation with varying regularization parameters. Since my prime goal in this project is to deliver an end-to-end system, rather than only an optimally trained "supermodel", I did not try out any other algorithm here. This pragmatic choice helped us maintain focus throughout the project.
As evaluation metric, I chose the ROC area under the curve. To detect the most informative feature groups, I started, in a 10-fold cross validation setting, from an information-less baseline model with 50% ROC AUC performance. I then tested each of the remaining feature groups independently, selecting the best one as the "constant" feature group for the next round. There, it was on its turn combined with each of the remaining feature groups; again, a winner was selected and added to the ranked list of feature groups. The end result of this iterative process is the following feature group ranking, in descending order of marginal contribution:
| Type | Feature group | ROC AUC | Percentage point improvement |
|---|---|---|---|
| — | Baseline | 50.00 % | — |
| Loan | Log10LoanAmount | 76,50 % | + 26.50 |
| SDLA | 20 loan description topics | 80.72 % | + 4.22 |
| Loan | PostedMonth[Jan..Dec] | 83.55 % | + 2.83 |
| Loan | MajorityGender | 85.99 % | + 2.44 |
| Loan | Log10NumberOfBorrowers | 87.13 % | + 1.14 |
| Partner | LoansPosted & TotalAmountRaised | 87.42 % | + 0.29 |
| Loan | GeoLongitude & GeoLatitude | 87.63 % | + 0.21 |
| Loan | RepaymentTerm | 87.77 % | + 0.14 |
| Partner | DelinquencyRate & Rating | 87.77 % | + 0.00 |
| Loan | BonusCreditElegibility | 87.82 % | + 0.05 |
A few observations:
Also of interest is the feature polarity, i.e. whether a feature influences a positive prediction (= full funding) in the positive or negative sense. Let's look successively at the "PostedMonth" features, the topic features, and all the other ones.
All other things being equal, it is easier to get loans fully funded that are posted in the first three months of the calendar year. September and June are the least advantageous.
Twelve topics contribute positively towards full funding, the remaining eight negatively. The extent to which a specific loan description contributes in either direction, of course also depends of on the actual distribution of topics found in that particular document.
Each topic is represented here by its ten most prominent words.
A positive contributor to full funding is the total amount of money raised by the field partner: this can be interpreted as a proxy for the partner's longevity and reliability. On the other hand, the number of loans (LoansPosted) that went through that same field partner's hands negatively impacts full funding - strangely so.
The more borrowers take part in a loan request, the better its chanches to get fully funded. This can be interpreted as a form of risk reduction, the idea being that multiple borrowers will help and/or control each other, thereby increasing loan quality. Otherwise said: lenders tend to appreciate and foster collaboration between borrowers - with their money.
In my system, gender is encoded as female=0, and male=1. The negative impact of the MajorityGender feature therefore confirms the initial hypothesis that male borrowers have a harder time getting their predisbursed loan requests backfilled.
The feature with the most important negative impact across the whole feature set (including PostedMonth and topic features) is the loan amount: the more money is requested, the harder it is to get the loan fully funded. Assuming a constant allocatable sum per lender at a particular moment in time, it is logical that more lenders are required then to backfill a larger loan amount. If not enough lenders are found within the expiration period, the loan does not get fully funded.
In order to facilitate deployment of the model in a live setting, and maximize code reuse, all relevant data preprocessing, feature creation (especially for SLDA), and feature scaling functionality was cleanly implemented in separate Python modules:
Finally, the best performing model was pickled and stored in GitHub. The main script that runs model training and evaluation is loan_funding_predictor.py. See the iPython notebook for its usage.
| Technology | Used for | Useful for |
|---|---|---|
| GitHub | Storage of the best performing model | Lightweight deployment to the (until further notice transient) cloud environment |
| pickle | Deserialization of the trained model | Loading the trained model in the live web app |
| SLDA (Supervised Latent Dirichlet Allocation) | Inference of gamma values against a pre-trained auxiliary topic model | On-the-fly feature generation for previously unseen loan descriptions |
| Flask and Flask-RESTful | Setup of live RESTful web application | Serving on-the-fly predictions for previously unseen Kiva loan requests |
Deployment of the software and prediction model to the virtual machine at Digital Ocean was simply done by logging onto that machine, and pulling all relevant files from GitHub. The trained model itself was unpickled and loaded into a Flask-based web application. Obviously, the virtual box also includes preinstalled copies of Python, all required libraries (including sklearn), and a precompiled slda binary.
This step is simply a retrieval of a new loan page from the Kiva website. Kiva offers its lenders a constant stream of recently predisbursed loan requests from which to choose.
The following steps describe how we can — at least virtually — augment each loan page with live information from the loan funding predictor.
Disclaimer: at the time of writing, and until further notice, the live environment is not meant to be running permanently. The backend will only be live during planned demonstrations.
A typical backend request-response cycle looks like this:
Each loan funding prediction response returns:
Again, each topic is represented by its 10 most prominent words. In isolation, the gamma values say nothing about the propensity of a given loan to get funded or not. As explained earlier, the polarity or direction of the relationship between each topic and the class label is captured in the logistic regression model. At prediction time, the loan document's gamma values serve as input to the logistic regression model. This prediction happens together with all the other features in one go, of course.
Before the predictor could send the abovementioned response, it obviously needed some loan information to work on. In the current architecture, the predictor web application itself reaches out to the Kiva REST API, to get all relevant loan information on the fly.
To dynamically integrate the information received in step 5, I wrote a Google Chrome extension based on Kango and jQuery. Once it is installed and activated, the Kiva Loan Funding Visualization extension displays two new boxes inside any Kiva Loan page visited from that browser. It then looks like this:
The box in the upper-right corner contains the prediction score expressed as a (rounded) percentage, and a thumbs up/down image that represents the binary prediction. The treshold is currently set at 50%. The probability is called a priori, to stress that the actual funding status (displayed by Kiva) does not play any role in the predicted value. In other words, the predictor would predict the same probability before, during or after the funding period - with one exception: the field partner features (e.g. LoansPosted), may have changed over time.
The box below the borrower's image contains the three most prominent topics for the loan (description) at hand.
In this project, I have developed an end-to-end system that enables multiple actors in the Kiva ecosystem to get an a priori insight into the funding chances of previously unseen loans. While the current logistic regression model can no doubt be improved in various ways, its current performance of 87.82% ROC Area Under Curve is sufficient for a proof of concept.
As the virtual integration through a browser extension demonstrates, the system can indeed be deployed with minimum extra engineering effort, should Kiva wish to do so.
At the same time, the positive or negative contribution of each feature was identified. This may help Kiva actors in the field to adapt certain behaviours or revise certain decision strategies, should they feel the need to do so.
As far as deployment in the Kiva ecosystem is concerned, then, there will be more strategic and ethical questions to be answered, than technical ones.
Potential avenues for model performance improvement include:
Having come at the end of this bootcamp in New York City, I would like to thank:


Using the Raw and Binary content types.
The raw content type is similar to the HTMLText type, except that no new tags are added to the PubTal output, and so the author must enter all tags manually. For example:
title: Raw content test

This is the first paragraph - note the tags!

And this is the second paragraph.

To specify which files are treated as Raw please refer to the Configuration section.
The Binary content type does a straight copy of the content file to the destination directory, creating any required directories. No parsing of the content file is done and no template is used. To specify which files are treated as Binary please refer to the Configuration section.
PubTal Version 3.5


The editor shows the source code of the current project. In the edit mode (Section 3.1.1: Edit Mode) you can process on the code. The simulation mode (Section 3.1.2: Simulation Mode) shows the current simulation state in the editor window. Note that the simulation mode disables any processing in the editor.
The editor provides all common functions.
Mark, Cut, Copy and Paste. These functions are available as usual. The following table shows the shortcuts.
| Key(s) | Function |
|---|---|
| Arrow Keys | cursor movement |
| Ctrl + Arrow Keys Left and Right | cursor movement by words |
| <-- Backspace | delete character before cursor |
| Del | delete character under cursor |
| Pos 1 | move cursor to front of row |
| End | move cursor to end of row |
| Ctrl+Pos 1 | jump to beginning of listing |
| Ctrl+End | jump to end of listing |
| PgUp/PgDn | scroll by screen pages |
| Tab | emit tabulator spacing |
| Shift + Movement Keys | mark mode |
| Ctrl+A | mark all |
| Ctrl+C | copy marked region |
| Ctrl+X | cut marked region |
| Ctrl+V | insert from clipboard |
The editor is implemented using the Java class JTextArea. Key assignments are listed here.
During a simulation the current executed statements are highlighted.
Tabelle 2. Color marks
| Color | Description |
|---|---|
| Block | current executed cycle (Step) |
| Statement | current executed statement (Microstep) |
| Comparison | the comparison provides to be true (Microstep) |
| Comparison | the comparison provides to be false (Microstep) |
| Block | the cycle where the simulation is interrupted (Run) |
| Block | current reached breakpoint |
| Previous Page |


my name is anugrah
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Big Data the 'reactive' way
publisher
A metatrend going on in the IT industry is a shift from query-based, batch oriented systems to (soft) realtime updated systems. While this is associated with financial trading only, there are many other examples such as "Just-In-Time"-logistic systems, flight companies doing realtime pricing of passenger seats based on demand and load, C2C auction system like EBay, real time traffic control and many more.
It is likely this trend will continue, as the (commercial) value of information is time dependent, value decreases with age of information.
Automated trading in the finance sector is just a forerunner in this area, because some microseconds time advantage can be worth millions of dollars. Its natural real time processing systems evolve in this domain faster.
However big parts of traditional IT infrastructure is not designed for reactive, event based systems. From query based databases to request-response based Http protcol, the common paradigm is to store and query data "when needed".
Current Databases are static and query-oriented
Current approaches to data management such as SQL and NOSQL databases focus on data transactions and static query of data. Databases provide convenience in slicing and dicing data but they do not support update of complex queries in real time. Uprising NOSQL databases still focus on computing a static result.
Databases are clearly not "reactive".
Current Messaging Products provide poor query/filtering options
Current messaging products are weak at filtering. Messages are separated into different streams (or topics), so clients can do a raw preselection on the data received. However this frequently means a client application receives like 10 times more data than needed, doing fine grained filtering 'on-top'.
A big disadvantage is, that the topic approach builts filter capabilities "into" the system's data design.
E.g. if a stock exchange system splits streams on a per-stock base, a client application still needs to subscribe to all streams in order to provide a dynamically updated list of "most active" stocks. Querying usually means "replay+search the complete message history".
A scalable, "continuous query" distributed Datagrid.
I had the enjoyment to do conceptional & technical design for a large scale realtime system, so I'd like to share a generic scalable solution for continuous query processing at high volume and large scale.
It is common, that real-time processing systems are designed "event sourced". This means, persistence is replaced by journaling transactions. System state is kept in memory, the transaction journal is required for historic analysis and crash recovery only.
Client applications do not query, but listen to event streams instead. A common issue with event sourced systems is the problem of "late joining client". A late client would have to replay the whole system event journal in order to get an up-to-date snapshot of the system state.
In order to support late joining clients, a kind of "Last Value Cache" (LVC) component is required. The LVC holds current system state and allows late joiners to bootstrap by querying.
In a high performance, large data system, the LVC component becomes a bottleneck as the number of clients rises.
Generalizing the Last Value Cache: Continuous Queries
In a continuous query data cache, a query result is kept up to date automatically. Queries are replaced by subscriptions.
subscribe * from Orders where
symbol in ['ALV', 'BMW'] and
volume > 1000 and
owner='MyCompany'
creates a message stream, which initially performs a query operation, after that updates the result set whenever a data change affecting the query result happened (transparent to the client application). The system ensures each subscriber receives exactly the change notifications necessary to keep its "live" query results up-to-date.
|
| A distributed continous query system: The LVC Nodes hold data. Transactions are sent to them on a message bus (red). The LVC nodes compute the actual difference caused by a transaction and send change notifications on a message bus (blue). This enables "processing nodes" to keep a mirror of their relevant data partition up-to-date. External clients connected via TCP/Http do not listen to the message bus (because multicast is not an option in WAN). "Subscription processors" keep the client's continuous queries up-to-date by listening to the (blue) message bus and dispatching required change notifications only to client's point2point connection. |
Difference of data access patterns compared to static data management:
- High write volume
Real time systems create a high volume of write access/change in data.
- Fewer full table scans.
Only late-joining clients or changes of a query's condition require a full data scan. Because continuous queries make "refreshing" a query result obsolete, Read/Write ratio is ~ 1:1 (if one counts the change notification resulting from a transaction as "Read Access").
- The majority of load is generated, when evaluating queries of active continuous subscriptions with each change of data. Consider a transaction load of 100.000 changes per second with 10.000 active continuous queries: this requires 100.000*10.000 = 1 Billion evaluations of query conditions per second. That's still an underestimation: When a record gets updated, it must be tested whether the record has matched a query condition before the update and whether it matches after the update. A record's update may result in an add (because it matches after the change) or a remove transaction (because the record does not match anymore after a change) to a query subscription (or 'update', or 'skip' ofc).
Data Cluster Nodes ("LastValueCache Nodes")
Data is organized in tables, column oriented. Each table's data is evenly partitioned amongst all data grid nodes (=last value cache node="LVC node"). By adding data nodes to the cluster, capacity is increased and snapshot queries (initializing a subscription) are sped up by increased concurrency.
There are three basic transactions/messages processed by the data grid nodes:
- AddRow(table,newRow),
- RemoveRow(table,rowId),
- UpdateRow(table, rowId, diff).
The data grid nodes provide a lambda-alike (row iterator) interface supporting the iteration of a table's rows using plain java code. This can be used to perform map-reduce jobs and as a specialization, the initial query required by newly subscribing clients. Since ongoing computation of continuous queries is done in the "Gateway" nodes, the load of data nodes and the number of clients correlate weakly only.
All transactions processed by a data grid node are (re-)broadcasted using multicast "Change Notification" messages.
Gateway Nodes
Gateway nodes track subscriptions/connections to client applications. They listen to the global stream of change notifications and check whether a change influences the result of a continuous query (=subscription). This is very CPU intensive.
Two things make this work:
- by using plain java to define a query, query conditions profit from JIT compilation, no need to parse and interpret a query language. HotSpot is one of the best optimizing JIT compilers on the planet.
- Since multicast is used for the stream of global changes, one can add additional Gateway nodes with ~no impact on throughput of the cluster.
Processor (or Mutator) Nodes
These nodes implement logic on-top of the cluster data. E.g. a statistics processor does a continuous query for each table, incrementally counts the number of rows of each table and writes the results back to a "statistics" table, so a monitoring client application can subscribe to realtime data of current table sizes. Another example would be a "Matcher processor" in a stock exchange, listening to orders for a stock, if orders match, it removes them and adds a Trade to the "trades" table.
If one sees the whole cluster as kind of a "giant spreadsheet", processors implement the formulas of this spreadsheet.
Scaling Up
- with data size:
increase number of LVC nodes
- Number of Clients
increase subscription processor nodes.
- TP/S
scale up processor nodes and LVC nodes
Of cause the system relies heavily on availability of a "real" multicast messaging bus system. Any point to point oriented or broker-oriented networking/messaging will be a massive bottleneck.
Conclusion
Building real time processing software backed by a continuous query system simplifies application development a lot.
- Its model-view-controller at large scale.
Astonishing: patterns used in GUI applications for decades have not been extended regulary to the backing data storage systems.
- Any server side processing can be partitioned in a natural way. A processor node creates an in-memory mirror of its data partition using continuous queries. Processing results are streamed back to the data grid. Computing intensive jobs, e.g. risk computation of derivatives can be scaled by adding processor instances subscribing to distinct partitions of the data ("sharding").
- The size of the Code Base reduces significantly (both business logic and Front-End).
A lot of code in handcrafted systems deals with keeping data up to date.
About me
I am a technical architect/senior developer consultant at an european company involved heavily in stock & derivative trading systems.
|
|


Use Grep to Drop Columns by Guangming Lang ~1 min read January 27, 2017 Categories r It’s very easy to drop columns from a data frame using the grep() function. The following example shows you how. # print the first 3 records of the iris dataset head(iris, 3) ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3.0 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa # grep the indices of the columns with "Sepal" in their names sepal_col_num = grep("Sepal", names(iris)) # drop these columns iris_sub = iris[, -sepal_col_num] head(iris_sub, 3) ## Petal.Length Petal.Width Species ## 1 1.4 0.2 setosa ## 2 1.4 0.2 setosa ## 3 1.3 0.2 setosa You can also use grep() to extract column names. # grep the names of the columns with "Petal" in their names grep("Petal", names(iris), value=T) ## [1] "Petal.Length" "Petal.Width" Share Tweet LinkedIn Reddit Previous Why Models Fail Next Prediction vs. Interpretability


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Trees, Tries, and Trying my Patience…
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How You Should Start Every Coding Project
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Importance of Considering Scaling: Big O Notation with Python
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Pressure
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"She Was"
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Searching for the Eye of the Storm
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A New Technology and Society
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What to Expect from Each Other
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Observe. Emulate. Adapt. : Knights’ Theorem
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A Piece of My Pride
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Descending: A Shakedown of Linear Regression in Python
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The Other 3D: Deploying Django with Docker
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Generating Fractals with Recursion: Python and Processing.py edition
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Machine Learning for Societal Advancement
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7 Lessons I’ve Re-learned
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Living a Life Without Fear
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Finding Purpose
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Environment vs. Environment
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Sanity. Strength. Humanity. : The Effects of Rapidly Changing Technology
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The Acclimation and Legality of Superior Machines
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--- layout: base title: Arizona Karen Baptist Church link: bible_study_schedule permalink: /bible_study_schedule/ ---


{% extends "sentry/emails/base.html" %} {% load i18n %} {% load sentry_avatars %} {% load sentry_helpers %} {% block main %}
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--- type: "youtube" title: "The Cleaner App clean and boot mobile" yt-video-id: "S7nmh4vZLbU" description: "The Cleaner App clean and boot mobile" social_media_msg: "..." featimg: "http://images.dabannit.com/da0f02/2016-03-24-the-cleaner-apps.html-6ef.jpg" article_videos: "" origin_url: "https://www.youtube.com/watch?v=S7nmh4vZLbU" ---
Cleaner App : clean and boot mobile
it is a FREE app tha can boost your Android’s speed, clean memory, clear out junk from your download folder, free up storage to optimize your device, increase your security, improve performance of your mobile.p>


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| Package | Description |
|---|---|
| wordcram |
| Modifier and Type | Method and Description |
|---|---|
Word[] |
WordCram.getSkippedWords()
Returns an array of words that could not be placed.
|
Word |
WordCram.getWordAt(float x,
float y)
Get the Word at the given (x,y) coordinates.
|
Word[] |
WordCram.getWords()
Get the Words that WordCram is drawing.
|
Word |
Word.setAngle(float angle)
Set the angle this Word should be rendered at - WordCram won't even call the WordAngler.
|
Word |
Word.setColor(int color)
Set the color this Word should be rendered in - WordCram won't call the WordColorer.
|
Word |
Word.setFont(processing.core.PFont font)
Set the font this Word should be rendered in - WordCram won't call the WordFonter.
|
Word |
Word.setPlace(float x,
float y)
Set the place this Word should be rendered at - WordCram won't call the WordPlacer.
|
Word |
Word.setPlace(processing.core.PVector place)
Set the place this Word should be rendered at - WordCram won't call the WordPlacer.
|
Word |
Word.setProperty(java.lang.String propertyName,
java.lang.Object propertyValue)
Set a property on this Word, to be used by a WordColorer, a WordPlacer, etc, down the line.
|
Word |
Word.setSize(float size)
Set the size this Word should be rendered at - WordCram won't even call the WordSizer.
|
| Modifier and Type | Method and Description |
|---|---|
float |
WordAngler.angleFor(Word word)
What angle should this
Word be rotated at? |
int |
WordColorer.colorFor(Word word)
What color should this
Word be? |
int |
Word.compareTo(Word w)
Compares Words based on weight only.
|
processing.core.PFont |
WordFonter.fontFor(Word word)
What font should this
Word be drawn in? |
WordCram |
WordCram.fromWords(Word[] words)
Makes a WordCram from your own custom Word array.
|
processing.core.PVector |
WordNudger.nudgeFor(Word word,
int attemptNumber)
How should this word be nudged, this time?
|
processing.core.PVector |
SpiralWordNudger.nudgeFor(Word w,
int attempt) |
processing.core.PVector |
RandomWordNudger.nudgeFor(Word w,
int attempt) |
processing.core.PVector |
PlottingWordNudger.nudgeFor(Word word,
int attempt) |
processing.core.PVector |
WordPlacer.place(Word word,
int wordIndex,
int wordsCount,
int wordImageWidth,
int wordImageHeight,
int fieldWidth,
int fieldHeight)
Where should this
Word be drawn on the field? |
processing.core.PVector |
WaveWordPlacer.place(Word word,
int wordIndex,
int wordsCount,
int wordImageWidth,
int wordImageHeight,
int fieldWidth,
int fieldHeight) |
processing.core.PVector |
UpperLeftWordPlacer.place(Word word,
int wordIndex,
int wordsCount,
int wordImageWidth,
int wordImageHeight,
int fieldWidth,
int fieldHeight) |
processing.core.PVector |
SwirlWordPlacer.place(Word word,
int wordIndex,
int wordsCount,
int wordImageWidth,
int wordImageHeight,
int fieldWidth,
int fieldHeight) |
processing.core.PVector |
PlottingWordPlacer.place(Word word,
int wordIndex,
int wordsCount,
int wordImageWidth,
int wordImageHeight,
int fieldWidth,
int fieldHeight) |
float |
WordSizer.sizeFor(Word word,
int wordRank,
int wordCount)
How big should this
Word be rendered? |


Numbers 7:12
And he that offered his offering the first day was Nahshon the son of Amminadab, of the tribe of Judah:


| Author: | Cecilie Fritzvold |
|---|---|
| Author: | Per Buer |
| Date: | 2010-05-31 |
| Version: | 1.0 |
| Manual section: | 1 |
varnishreplay [-D] -a address:port -r file
The varnishreplay utility parses varnish logs and attempts to reproduce the traffic. It is typcally used to warm up caches or various forms of testing.
The following options are available:
| -a backend | Send the traffic over tcp to this server, specified by an address and a port. This option is mandatory. Only IPV4 is supported at this time. |
| -D | Turn on debugging mode. |
| -r file | Parse logs from this file. This option is mandatory. |
The varnishreplay utility and this manual page were written by Cecilie Fritzvold and later updated by Per Buer.
This document is licensed under the same licence as Varnish itself. See LICENCE for details.


BasicBlockchain
public abstract class Blockchain
extends java.lang.Object
| Constructor | Description |
|---|---|
Blockchain(int creatorID,
int channel) |
Constructor
|
Blockchain(Blockchain orig,
int start,
int end) |
Copy constructor.
|
| Modifier and Type | Method | Description |
|---|---|---|
void |
addBlock(toy.proto.Types.Block b) |
Add block to the head of the chain.
|
abstract Block |
createNewBLock() |
Define the way to create a new block.
|
toy.proto.Types.Block |
getBlock(int index) |
Get the index_th block of the chain.
|
java.util.List<toy.proto.Types.Block> |
getBlocks(int start,
int end) |
Get a sublist of blocks.
|
int |
getHeight() |
Get the current BLockchain height
|
void |
removeBlock(int index) |
Remove the index_th block.
|
void |
setBlock(int index,
toy.proto.Types.Block b) |
Replace a single block with a new block.
|
void |
setBlocks(java.util.List<toy.proto.Types.Block> Nblocks,
int start) |
Replace sub-chain with another sub-chain.
|
boolean |
validateBlockCreator(toy.proto.Types.Block b,
int f) |
Check if a block was proposed by a leader that proposed one of the last f+1 blocks.
|
boolean |
validateBlockHash(toy.proto.Types.Block b) |
Validate a block hash with respect to the latest block in the chain.
|
boolean |
validateCurrentLeader(int leader,
int f) |
Check if leader is one of the last f+1 proposers.
|
public Blockchain(int creatorID, int channel)
creatorID
- the creator server IDchannel
- the creator server channel (with a single Toy group this is always 0)public Blockchain(Blockchain orig, int start, int end)
orig
- the original Blockchainstart
- the index to start fromend
- the index to end withpublic abstract Block createNewBLock()
public boolean validateCurrentLeader(int leader, int f)
leader
- the leader to be validatedf
- an upper bound on the faulty nodes numberpublic boolean validateBlockCreator(toy.proto.Types.Block b, int f)
b
- block to be validatedf
- an upper bound on the faulty nodes numberpublic void setBlocks(java.util.List<toy.proto.Types.Block> Nblocks, int start)
Nblocks
- the new sub-chainstart
- index from where to start replacingpublic void setBlock(int index, toy.proto.Types.Block b)
index
- the index to be replacedb
- the nre blockpublic boolean validateBlockHash(toy.proto.Types.Block b)
b
- the block to be validatedpublic void addBlock(toy.proto.Types.Block b)
b
- the block to addpublic toy.proto.Types.Block getBlock(int index)
index
- the index of the requested blockpublic java.util.List<toy.proto.Types.Block> getBlocks(int start, int end)
start
- the index to start withend
- the index to end withpublic int getHeight()
public void removeBlock(int index)
index
- the block to be removedCopyright © 2018. All rights reserved.


Since the DES algorithm was announced in the 1970s it was criticized for its short key length of 56 bit. In order to enlarge the key space of DES, Ron Rivest (the "R" in RSA) came up with the DESX proposal already in the mid-1980s. A message that is to be DESX encrypted is XORed with a 64-bit pre-whitening key K1 before it is encrypted with DES using a 56-bit key KDES. Finally, the result is XORed with a post-whitening key K2.
DESX(x) = K2 + DESKDES(x + K1)
The key space is extended to 184 bits due to the two 64-bit pre- and post-whitening keys. However, Kilian and Rogaway proved in [KR96] that the security level of DESX equals a key length of 118 bits. Compared to a 56-bit security level of the original DES this is still a remarkable improvement.
The simplicity of DESX is one of its main advantages: Just two simple XOR gates are additionally required. XOR gates can be implemented very efficiently both in hardware and in software without a significant loss of performance. Furthermore, when K1 = K2 = 0 is chosen, DESX is compliant to DES.
DESX is used with a slightly modified key in the SAFER security toolkit by RSA. The 64-bit pre-whitening key K1 and the DES key KDES are concatenated and fed into a one-way function. The 64-bit output of this one-way function serves as the post-whitening key K2.
For further information see: http://www.crypto.rub.de/en_publications.html
CrypTool uses DESX in CBC mode mit zero initialization vector and 01-00 padding.
It expects key entry in the order KDES, K1, K2. E. g. the input
00 00 00 00 00 00 00 00 11 11 11 11 11 11 11 11 22 22 22 22 22 22 22 22is interpreted as KDES = 00 ... 00, K1 = 11 ... 11 and K2 = 22 ... 22.
References:
[KR96a] www.cs.ucdavis.edu/~rogaway/papers/desx.ps
[KR96b] Phillip Rogaway, RSA Laboratories' CryptoBytes, Summer 1996
www.cs.ucdavis.edu/~rogaway/papers/cryptobytes.ps


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|
MoveToCode
|
Script used to start and stop recording sessions in the current dictation system and report the transcribed text via UnityEvents. More...
Classes | |
| class | StringUnityEvent |
Public Member Functions | |
| void | StartRecording () |
| Start a recording session in the dictation system. More... | |
| void | StopRecording () |
| Stop a recording session in the dictation system. More... | |
Public Attributes | |
| StringUnityEvent | OnDictationHypothesis |
| Event raised while the user is talking. More... | |
| StringUnityEvent | OnDictationResult |
| Event raised after the user pauses, typically at the end of a sentence. More... | |
| StringUnityEvent | OnDictationComplete |
| Event raised when the recognizer stops. More... | |
| StringUnityEvent | OnDictationError |
| Event raised when an error occurs. More... | |
Protected Member Functions | |
| override void | RegisterHandlers () |
| Overload this method to specify, which global events component wants to listen to.Use RegisterHandler API of InputSystem More... | |
| override void | UnregisterHandlers () |
| Overload this method to specify, which global events component should stop listening to.Use UnregisterHandler API of InputSystem More... | |
| override void | Start () |
| override void | OnDisable () |
| Protected Member Functions inherited from Microsoft.MixedReality.Toolkit.Input.BaseInputHandler | |
| override void | OnEnable () |
| virtual void | Update () |
| Protected Member Functions inherited from Microsoft.MixedReality.Toolkit.Input.InputSystemGlobalHandlerListener | |
| async Task | EnsureInputSystemValid () |
| A task that will only complete when the input system has in a valid state. More... | |
Properties | |
| bool | IsListening [get] |
| Get the listening state More... | |
| AudioClip | AudioClip [get] |
| Get the last audio clip from the dictation session More... | |
| Properties inherited from Microsoft.MixedReality.Toolkit.Input.BaseInputHandler | |
| virtual bool | IsFocusRequired [get, protected set] |
| Is Focus required to receive input events on this GameObject? More... | |
Script used to start and stop recording sessions in the current dictation system and report the transcribed text via UnityEvents.
For this script to work, a dictation system like 'Windows Dictation Input Provider' must be added to the Data Providers in the Input System profile.
|
protectedvirtual |
Reimplemented from Microsoft.MixedReality.Toolkit.Input.BaseInputHandler.
|
protectedvirtual |
Overload this method to specify, which global events component wants to listen to.Use RegisterHandler API of InputSystem
Implements Microsoft.MixedReality.Toolkit.Input.InputSystemGlobalHandlerListener.
|
protectedvirtual |
Reimplemented from Microsoft.MixedReality.Toolkit.Input.BaseInputHandler.
| void Microsoft.MixedReality.Toolkit.Input.DictationHandler.StartRecording | ( | ) |
Start a recording session in the dictation system.
| void Microsoft.MixedReality.Toolkit.Input.DictationHandler.StopRecording | ( | ) |
Stop a recording session in the dictation system.
|
protectedvirtual |
Overload this method to specify, which global events component should stop listening to.Use UnregisterHandler API of InputSystem
Implements Microsoft.MixedReality.Toolkit.Input.InputSystemGlobalHandlerListener.
| StringUnityEvent Microsoft.MixedReality.Toolkit.Input.DictationHandler.OnDictationComplete |
Event raised when the recognizer stops.
Contains the final recognized string.
| StringUnityEvent Microsoft.MixedReality.Toolkit.Input.DictationHandler.OnDictationError |
Event raised when an error occurs.
Contains the string representation of the error reason.
| StringUnityEvent Microsoft.MixedReality.Toolkit.Input.DictationHandler.OnDictationHypothesis |
Event raised while the user is talking.
As the recognizer listens, it provides text of what it's heard so far.
| StringUnityEvent Microsoft.MixedReality.Toolkit.Input.DictationHandler.OnDictationResult |
Event raised after the user pauses, typically at the end of a sentence.
Contains the full recognized string so far.
|
get |
Get the last audio clip from the dictation session
|
get |
Get the listening state


com.qingstor.sdk.upload
接口
Recorder
UploadProgressListener
类
UploadManager
UploadManager.CompleteMultipartUploadInput
UploadManager.PutObjectInput
UploadManagerCallback