Logistic Regression with Apache Spark
Logistic regression is widely used to predict binary responses. It is a linear method as described in my other post An Overview of Logistic Regression.
Binary logistic regression can be generalized into multinomial logistic regression to train and predict multiclass classification problems. For example, for K possible outcomes, one of the outcomes can be chosen as a “pivot”, and the other K−1 outcomes can be separately regressed against the pivot outcome. In MLlib, the first class 0 is chosen as the “pivot” class.
For multiclass classification problems, the algorithm will output a multinomial logistic regression model, which contains K−1 binary logistic regression models regressed against the first class. Given a new data points, K−1 models will be run, and the class with largest probability will be chosen as the predicted class.
Spark implemented two algorithms to solve logistic regression: gradient descent and L-BFGS. Here we focus on gradient descent.
Stochastic Gradient Descent (SGD)
Optimization problems whose objective function f is written as a sum are particularly suitable to be solved using stochastic gradient descent (SGD). For logistic regression, the optimization formulation commonly used:
where the logistic loss \( L \) is
The loss is written as an average of the individual losses coming from each data point.
A stochastic subgradient is a randomized choice of a vector, such that in expectation, we obtain a true subgradient of the original objective function. Picking one data point \( i\in [1,…,n] \) uniformly at random, we obtain a stochastic subgradient, with respect to \(\mathbf{w} \) as follows:
This is determine by the i-th data point. Running SGD simply becomes walking in the direction of the negative stochastic subgradient \( { f }’_{ w,i } \), that is
Step-size. The parameter \( \gamma \) is the step-size, which in the default implementation is
chosen decreasing with the square root of the iteration counter, i.e. \( \gamma =\frac { s }{ \sqrt { t } } \)
in the \(t\)-th iteration,
with the input parameter s = stepSize
. Note that selecting the best step-size
for SGD methods can often be delicate in practice and is a topic of active research.
Gradients. Here is a table of (sub)gradients of the machine learning methods implemented in MLlib.
Update Schemes for Distributed-SGD
The SGD implementation in GradientDescent uses a simple distributed sampling of the data points.
Recall that the loss part of the optimization problem
is $ \frac { 1 }{ n } \sum _{ i=1 }^{ n }{ L(\mathbf{w};{ \mathbf{x} }_{ i },{ y }_{ i }) } $,
and therefore $ \frac { 1 }{ n } \sum _{ i=1 }^{ n }{ { L }’_{ w,i } } $ would be the true
subgradient. Since this would require the access to the full data set, the parameter miniBatchFraction
specifies which fraction of the full data to use instead.
The average of the gradients over this subset, i.e.
is a stochastic gradient. Here \( S \) is the sampled subset of size
\( \left| S \right|= \) miniBatchFraction
\( \cdot n \)
In each iteration, the sampling over the distributed dataset (RDD), as well as the computation of the sum of the partial results from each worker machine is performed by the standard spark routines.
If the fraction of points miniBatchFraction
is set to 1 (default), then the resulting step in each
iteration is exact (sub)gradient descent. In this case there is no randomness and no variance in the
used step directions.
On the other extreme, if miniBatchFraction is chosen very small, such that only
a single point is sampled, i.e. \( \left| S \right|= \) miniBatchFraction
\( \cdot n = 1\),
then the algorithm is equivalent to standard SGD.
In that case, the step direction depends from the uniformly random sampling of the point.
SGD Implementation in MLlib
class pyspark.mllib.classification.LogisticRegressionWithSGD
classmethod train(data, iterations=100, step=1.0, miniBatchFraction=1.0, initialWeights=None, regParam=0.01, regType=’l2’, intercept=False, validateData=True)
Train a logistic regression model on the given data.
Parameters:
data
– The training data, an RDD of LabeledPoint.iterations
– The number of iterations (default: 100).step
– The step parameter used in SGD (default: 1.0).miniBatchFraction
– Fraction of data to be used for each SGD iteration (default: 1.0).initialWeights
– The initial weights (default: None).regParam
– The regularizer parameter (default: 0.01).regType
– The type of regularizer used for training our model. Allowed values:- “l1” for using L1 regularization
- “l2” for using L2 regularization
- None for no regularization
- (default: “l2”)
- intercept – Boolean parameter which indicates the use or not of the augmented representation for training data (i.e. whether bias features are activated or not, default: False).
- validateData – Boolean parameter which indicates if the algorithm should validate data before training. (default: True)
Click-Through Rate (CTR) Prediction with MLlib
Here I will show an example of utilizing LogisticRegressionWithSGD to predict CTR given some training examples. The dataset is provided by Criteo Labs to a Kaggle competition. It has roughly 51M data points, splitted into 39M for training, 6M for validation and 6M for test. Each data point has 233K one-hot-encoded binary features (originally some features had a huge number of categories before one-hot-encoding).
In MLlib, each labeled data point is represented as the object
LabeledPoint(label, featureVector)
. The training set is an RDD of a lot of LabeledPoint
objects,
OHETrainData
.
First, use LogisticRegressionWithSGD
to train a model using OHETrainData
with
the given hyperparameter configuration. LogisticRegressionWithSGD
returns a
LogisticRegressionModel.
Next, use the LogisticRegressionModel.weights
and LogisticRegressionModel.intercept
attributes to
print out the model’s parameters.
from pyspark.mllib.classification import LogisticRegressionWithSGD
# fixed hyperparameters
numIters = 50
stepSize = 10.
regParam = 1e-6
regType = 'l2'
includeIntercept = True
model0 = LogisticRegressionWithSGD.train(OHETrainData, iterations=numIters, step=stepSize,
regParam=regParam, regType=regType, intercept=includeIntercept)
sortedWeights = sorted(model0.weights)
print sortedWeights[:5], model0.intercept
'''
output:
[-0.4589923685357562, -0.37973707648623972, -0.3699655826675331, -0.36934962879928285, -0.32697945415010637] 0.56455084025
'''
log loss is used to evaluate the model. To compute the log loss, we first need the predictions. To generate predictions from our trained model, we use the model’s weight and intercept attributes and pass them into the sigmoid function.
from math import exp # exp(-t) = e^-t
def getP(x, w, intercept):
"""Calculate the probability for an observation given a set of weights and intercept.
Note:
We'll bound our raw prediction between 20 and -20 for numerical purposes.
Args:
x (SparseVector): A vector with values of 1.0 for features that exist in this
observation and 0.0 otherwise.
w (DenseVector): A vector of weights (betas) for the model.
intercept (float): The model's intercept.
Returns:
float: A probability between 0 and 1.
"""
rawPrediction = w.dot(x) + intercept
# Bound the raw prediction value
rawPrediction = min(rawPrediction, 20)
rawPrediction = max(rawPrediction, -20)
pred = (1 + exp(-rawPrediction))**(-1)
return pred
trainingPredictions = OHETrainData.map(lambda point: getP(point.features, model0.weights, model0.intercept))
print trainingPredictions.take(5)
'''
output:
[0.30262882023911125, 0.10362661997434075, 0.283634247838756, 0.17846102057880114, 0.5389775379218853]
'''
Now the \( p \)’s are calculated (probabilities for the data points to be in class y = 1), the log loss can be computed with \( p \) and the true labels \( y \).
from math import log
def computeLogLoss(p, y):
"""Calculates the value of log loss for a given probabilty and label.
Note:
log(0) is undefined, so when p is 0 we need to add a small value (epsilon) to it
and when p is 1 we need to subtract a small value (epsilon) from it.
Args:
p (float): A probabilty between 0 and 1.
y (int): A label. Takes on the values 0 and 1.
Returns:
float: The log loss value.
"""
epsilon = 10e-12
if p == 0:
p += epsilon
if p == 1:
p -= epsilon
return -y * log(p) - (1 - y) * log(1-p)
classOneFracTrain = OHETrainData.map(lambda point: point.label).mean()
print classOneFracTrain
logLossTrBase = OHETrainData.map(lambda point: computeLogLoss(classOneFracTrain, point.label)).mean()
print 'Baseline Train Logloss = {0:.3f}\n'.format(logLossTrBase)
'''
output:
0.22717773523
Baseline Train Logloss = 0.536
'''
def evaluateResults(model, data):
"""Calculates the log loss for the data given the model.
Args:
model (LogisticRegressionModel): A trained logistic regression model.
data (RDD of LabeledPoint): Labels and features for each observation.
Returns:
float: Log loss for the data.
"""
w = model.weights
intercept = model.intercept
logloss = data.map(lambda point: computeLogLoss(getP(point.features, w, intercept), point.label)).mean()
return logloss
logLossTrLR0 = evaluateResults(model0, OHETrainData)
print ('OHE Features Train Logloss:\n\tBaseline = {0:.3f}\n\tLogReg = {1:.3f}'
.format(logLossTrBase, logLossTrLR0))
'''
output:
OHE Features Train Logloss:
Baseline = 0.536
LogReg = 0.457
'''
classOneFracVal = OHEValidationData.map(lambda point: point.label).mean()
logLossValBase = OHEValidationData.map(lambda point: computeLogLoss(classOneFracTrain, point.label)).mean()
logLossValLR0 = evaluateResults(model0, OHEValidationData)
print ('OHE Features Validation Logloss:\n\tBaseline = {0:.3f}\n\tLogReg = {1:.3f}'
.format(logLossValBase, logLossValLR0))
'''
output:
OHE Features Validation Logloss:
Baseline = 0.528
LogReg = 0.457
'''
After feature hashing and training several models for different step-sizes and regularization constants, the evaluation of the best model is shown below.
logLossTest = evaluateResults(bestModel, hashTestData)
# Log loss for the baseline model
classOneFracTest = hashTestData.map(lambda point: point.label).mean()
logLossTestBaseline = hashTestData.map(lambda point: computeLogLoss(classOneFracTest, point.label)).mean()
print ('Hashed Features Test Log Loss:\n\tBaseline = {0:.3f}\n\tLogReg = {1:.3f}'
.format(logLossTestBaseline, logLossTest))
'''
output:
Hashed Features Test Log Loss:
Baseline = 0.537
LogReg = 0.456
'''


Uses of Interface
org.apache.shiro.subject.Subject
-
Packages that use Subject Package Description org.apache.shiro This package primarily exists as a root classpath distinction, but it does contain two core classes widely used by applications,SecurityUtils
andShiroException
.org.apache.shiro.aop Components used to support the framework's AOP/interception support classes.org.apache.shiro.cas org.apache.shiro.concurrent Executor
,ExecutorService
, andScheduledExecutorService
implementations for transparentSubject
association with threads in an asynchronous execution environment.org.apache.shiro.mgt Provides the masterSecurityManager
interface and a default implementation hierarchy for managing all aspects of Shiro's functionality in an application.org.apache.shiro.samples org.apache.shiro.subject Components supporting theSubject
interface, the most important concept in Shiro's API.org.apache.shiro.subject.support Concrete support implementations of most of theorg.apache.shiro.subject
interfaces.org.apache.shiro.util Your run-of-the-mill 'util' package for components and logic widely used across the framework that can't find their home into a proper OO hierarchy (or, most likely for things used across many hierarchies).org.apache.shiro.web.filter Base package supporting all ServletFilter
implementations used to control access to web pages and URL resources.org.apache.shiro.web.filter.authc ServletFilter
implementations specific to controlling access based on a subject's authentication status, or those that can execute authentications (log-ins) directly.org.apache.shiro.web.mgt Components supporting web-specificSecurityManager
implementations.org.apache.shiro.web.servlet Shiro-specific implementations of the Servlet API (Servlet Filters, et al).org.apache.shiro.web.subject Web-specificSubject
interfaces to enableSubject
use in web environments.org.apache.shiro.web.subject.support Supporting implementations oforg.apache.shiro.web.subject
package interfaces.org.apache.shiro.web.tags Provides the Shiro JSP Tag Library implementations. -
-
Uses of Subject in org.apache.shiro
Methods in org.apache.shiro that return Subject Modifier and Type Method Description static Subject
SecurityUtils. getSubject()
Returns the currently accessibleSubject
available to the calling code depending on runtime environment. -
Uses of Subject in org.apache.shiro.aop
Methods in org.apache.shiro.aop that return Subject Modifier and Type Method Description protected Subject
AnnotationHandler. getSubject()
Returns theSubject
associated with the currently-executing code.protected Subject
MethodInterceptorSupport. getSubject()
Returns theSubject
associated with the currently-executing code. -
Uses of Subject in org.apache.shiro.cas
Methods in org.apache.shiro.cas that return Subject Modifier and Type Method Description Subject
CasSubjectFactory. createSubject(SubjectContext context)
Deprecated.Methods in org.apache.shiro.cas with parameters of type Subject Modifier and Type Method Description protected boolean
CasFilter. onLoginSuccess(AuthenticationToken token, Subject subject, ServletRequest request, ServletResponse response)
Deprecated.If login has been successful, redirect user to the original protected url. -
Uses of Subject in org.apache.shiro.concurrent
Methods in org.apache.shiro.concurrent that return Subject Modifier and Type Method Description protected Subject
SubjectAwareExecutor. getSubject()
Returns the currently Subject instance that should be associated with Runnable or Callable instances before being dispatched to the targetExecutor
instance. -
Uses of Subject in org.apache.shiro.mgt
Methods in org.apache.shiro.mgt that return Subject Modifier and Type Method Description protected Subject
DefaultSecurityManager. createSubject(AuthenticationToken token, AuthenticationInfo info, Subject existing)
Creates aSubject
instance for the user represented by the given method arguments.Subject
DefaultSecurityManager. createSubject(SubjectContext subjectContext)
This implementation functions as follows:Subject
DefaultSubjectFactory. createSubject(SubjectContext context)
Subject
SecurityManager. createSubject(SubjectContext context)
Creates aSubject
instance reflecting the specified contextual data.Subject
SubjectFactory. createSubject(SubjectContext context)
Creates a new Subject instance reflecting the state of the specified contextual data.protected Subject
DefaultSecurityManager. doCreateSubject(SubjectContext context)
Actually creates aSubject
instance by delegating to the internalsubjectFactory
.Subject
DefaultSecurityManager. login(Subject subject, AuthenticationToken token)
First authenticates theAuthenticationToken
argument, and if successful, constructs aSubject
instance representing the authenticated account's identity.Subject
SecurityManager. login(Subject subject, AuthenticationToken authenticationToken)
Logs in the specified Subject using the givenauthenticationToken
, returning an updated Subject instance reflecting the authenticated state if successful or throwingAuthenticationException
if it is not.protected Subject
DefaultSubjectFactory. newSubjectInstance(PrincipalCollection principals, boolean authenticated, String host, Session session, SecurityManager securityManager)
Deprecated.since 1.2 - overrideDefaultSubjectFactory.createSubject(org.apache.shiro.subject.SubjectContext)
directly if you need to instantiate a customSubject
class.Subject
DefaultSubjectDAO. save(Subject subject)
Saves the subject's state to the subject'ssession
only ifsessionStorageEnabled(subject)
.Subject
SubjectDAO. save(Subject subject)
Persists the specified Subject's state for later access.Methods in org.apache.shiro.mgt with parameters of type Subject Modifier and Type Method Description protected void
DefaultSecurityManager. beforeLogout(Subject subject)
protected void
DefaultSecurityManager. bind(Subject subject)
Deprecated.in favor ofsave(subject)
.protected Subject
DefaultSecurityManager. createSubject(AuthenticationToken token, AuthenticationInfo info, Subject existing)
Creates aSubject
instance for the user represented by the given method arguments.protected void
DefaultSecurityManager. delete(Subject subject)
Removes (or 'unbinds') the Subject's state from the application, typically called duringDefaultSecurityManager.logout(org.apache.shiro.subject.Subject)
..void
DefaultSubjectDAO. delete(Subject subject)
Removes any existing subject state from the subject's session (if the session exists).void
SubjectDAO. delete(Subject subject)
Removes any persisted state for the specifiedSubject
instance.protected abstract void
AbstractRememberMeManager. forgetIdentity(Subject subject)
Forgets (removes) any remembered identity data for the specifiedSubject
instance.protected PrincipalCollection
AbstractRememberMeManager. getIdentityToRemember(Subject subject, AuthenticationInfo info)
boolean
DefaultSessionStorageEvaluator. isSessionStorageEnabled(Subject subject)
This implementation functions as follows: If the specified Subject already has an existingSession
(typically because an application developer has calledsubject.getSession()
already), Shiro will use that existing session to store subject state. If a Subject does not yet have a Session, this implementation checks thesessionStorageEnabled
property: IfsessionStorageEnabled
is true (the default setting), a new session may be created to persist Subject state if necessary. IfsessionStorageEnabled
isfalse
, a new session will not be created to persist session state. Most applications use Sessions and are OK with the defaulttrue
setting forsessionStorageEnabled
.protected boolean
DefaultSubjectDAO. isSessionStorageEnabled(Subject subject)
Determines if the subject's session will be used to persist subject state or not.boolean
SessionStorageEvaluator. isSessionStorageEnabled(Subject subject)
Returnstrue
if the specifiedSubject
'ssession
may be used to persist that Subject's state,false
otherwise.Subject
DefaultSecurityManager. login(Subject subject, AuthenticationToken token)
First authenticates theAuthenticationToken
argument, and if successful, constructs aSubject
instance representing the authenticated account's identity.Subject
SecurityManager. login(Subject subject, AuthenticationToken authenticationToken)
Logs in the specified Subject using the givenauthenticationToken
, returning an updated Subject instance reflecting the authenticated state if successful or throwingAuthenticationException
if it is not.void
DefaultSecurityManager. logout(Subject subject)
void
SecurityManager. logout(Subject subject)
Logs out the specified Subject from the system.protected void
DefaultSubjectDAO. mergeAuthenticationState(Subject subject)
Merges the Subject's current authentication state with whatever may be in any available session.protected void
DefaultSubjectDAO. mergePrincipals(Subject subject)
Merges the Subject's currentgetPrincipals()
with whatever may be in any available session.void
AbstractRememberMeManager. onFailedLogin(Subject subject, AuthenticationToken token, AuthenticationException ae)
Reacts to a failed login by immediatelyforgetting
any previously remembered identity.protected void
DefaultSecurityManager. onFailedLogin(AuthenticationToken token, AuthenticationException ae, Subject subject)
void
RememberMeManager. onFailedLogin(Subject subject, AuthenticationToken token, AuthenticationException ae)
Reacts to a failed authentication attempt, typically by forgetting any previously remembered principals for the Subject.void
AbstractRememberMeManager. onLogout(Subject subject)
Reacts to a subject logging out of the application and immediatelyforgets
any previously stored identity and returns.void
RememberMeManager. onLogout(Subject subject)
Reacts to a Subject logging out of the application, typically by forgetting any previously remembered principals for the Subject.void
AbstractRememberMeManager. onSuccessfulLogin(Subject subject, AuthenticationToken token, AuthenticationInfo info)
Reacts to the successful login attempt by first alwaysforgetting
any previously stored identity.protected void
DefaultSecurityManager. onSuccessfulLogin(AuthenticationToken token, AuthenticationInfo info, Subject subject)
void
RememberMeManager. onSuccessfulLogin(Subject subject, AuthenticationToken token, AuthenticationInfo info)
Reacts to a successful authentication attempt, typically saving the principals to be retrieved ('remembered') for future system access.void
AbstractRememberMeManager. rememberIdentity(Subject subject, AuthenticationToken token, AuthenticationInfo authcInfo)
Remembers a subject-unique identity for retrieval later.protected void
AbstractRememberMeManager. rememberIdentity(Subject subject, PrincipalCollection accountPrincipals)
Remembers the specified account principals by firstconverting
them to a byte array and thenremembers
that byte array.protected void
DefaultSecurityManager. rememberMeFailedLogin(AuthenticationToken token, AuthenticationException ex, Subject subject)
protected void
DefaultSecurityManager. rememberMeLogout(Subject subject)
protected void
DefaultSecurityManager. rememberMeSuccessfulLogin(AuthenticationToken token, AuthenticationInfo info, Subject subject)
protected abstract void
AbstractRememberMeManager. rememberSerializedIdentity(Subject subject, byte[] serialized)
Persists the identity bytes to a persistent store for retrieval later via theAbstractRememberMeManager.getRememberedSerializedIdentity(SubjectContext)
method.protected void
DefaultSubjectDAO. removeFromSession(Subject subject)
Removes any existing subject state from the Subject's session (if the session exists).protected void
DefaultSecurityManager. save(Subject subject)
Saves the subject's state to a persistent location for future reference if necessary.Subject
DefaultSubjectDAO. save(Subject subject)
Saves the subject's state to the subject'ssession
only ifsessionStorageEnabled(subject)
.Subject
SubjectDAO. save(Subject subject)
Persists the specified Subject's state for later access.protected void
DefaultSubjectDAO. saveToSession(Subject subject)
Saves the subject's state (it's principals and authentication state) to itssession
.protected void
DefaultSecurityManager. stopSession(Subject subject)
protected void
DefaultSecurityManager. unbind(Subject subject)
Deprecated.in Shiro 1.2 in favor ofDefaultSecurityManager.delete(org.apache.shiro.subject.Subject)
-
Uses of Subject in org.apache.shiro.samples
Methods in org.apache.shiro.samples that return Subject Modifier and Type Method Description Subject
WebApp. subject()
-
Uses of Subject in org.apache.shiro.subject
Methods in org.apache.shiro.subject that return Subject Modifier and Type Method Description Subject
Subject.Builder. buildSubject()
Creates and returns a newSubject
instance reflecting the cumulative state acquired by the other methods in this class.Subject
SubjectContext. getSubject()
Returns any existingSubject
that may be in use at the time the newSubject
instance is being created.Methods in org.apache.shiro.subject with parameters of type Subject Modifier and Type Method Description void
SubjectContext. setSubject(Subject subject)
Sets the existingSubject
that may be in use at the time the newSubject
instance is being created. -
Uses of Subject in org.apache.shiro.subject.support
Classes in org.apache.shiro.subject.support that implement Subject Modifier and Type Class Description class
DelegatingSubject
Implementation of theSubject
interface that delegates method calls to an underlyingSecurityManager
instance for security checks.Methods in org.apache.shiro.subject.support that return Subject Modifier and Type Method Description Subject
DefaultSubjectContext. getSubject()
protected Subject
SubjectThreadState. getSubject()
Returns theSubject
instance managed by thisThreadState
implementation.Methods in org.apache.shiro.subject.support with parameters of type Subject Modifier and Type Method Description void
DefaultSubjectContext. setSubject(Subject subject)
Constructors in org.apache.shiro.subject.support with parameters of type Subject Constructor Description SubjectCallable(Subject subject, Callable<V> delegate)
SubjectRunnable(Subject subject, Runnable delegate)
Creates a newSubjectRunnable
that, when executed, will execute the targetdelegate
, but guarantees that it will run associated with the specifiedSubject
.SubjectThreadState(Subject subject)
Creates a newSubjectThreadState
that will bind and unbind the specifiedSubject
to the thread -
Uses of Subject in org.apache.shiro.util
Methods in org.apache.shiro.util that return Subject Modifier and Type Method Description static Subject
ThreadContext. getSubject()
Convenience method that simplifies retrieval of a thread-bound Subject.static Subject
ThreadContext. unbindSubject()
Convenience method that simplifies removal of a thread-local Subject from the thread.Methods in org.apache.shiro.util with parameters of type Subject Modifier and Type Method Description static void
ThreadContext. bind(Subject subject)
Convenience method that simplifies binding a Subject to the ThreadContext. -
Uses of Subject in org.apache.shiro.web.filter
Methods in org.apache.shiro.web.filter that return Subject Modifier and Type Method Description protected Subject
AccessControlFilter. getSubject(ServletRequest request, ServletResponse response)
Convenience method that acquires the Subject associated with the request. -
Uses of Subject in org.apache.shiro.web.filter.authc
Methods in org.apache.shiro.web.filter.authc that return Subject Modifier and Type Method Description protected Subject
LogoutFilter. getSubject(ServletRequest request, ServletResponse response)
Returns the currently executingSubject
.Methods in org.apache.shiro.web.filter.authc with parameters of type Subject Modifier and Type Method Description protected String
LogoutFilter. getRedirectUrl(ServletRequest request, ServletResponse response, Subject subject)
Returns the redirect URL to send the user after logout.protected boolean
AuthenticatingFilter. onLoginSuccess(AuthenticationToken token, Subject subject, ServletRequest request, ServletResponse response)
protected boolean
FormAuthenticationFilter. onLoginSuccess(AuthenticationToken token, Subject subject, ServletRequest request, ServletResponse response)
-
Uses of Subject in org.apache.shiro.web.mgt
Methods in org.apache.shiro.web.mgt that return Subject Modifier and Type Method Description Subject
DefaultWebSubjectFactory. createSubject(SubjectContext context)
protected Subject
DefaultWebSubjectFactory. newSubjectInstance(PrincipalCollection principals, boolean authenticated, String host, Session session, ServletRequest request, ServletResponse response, SecurityManager securityManager)
Deprecated.since 1.2 - overrideDefaultWebSubjectFactory.createSubject(org.apache.shiro.subject.SubjectContext)
directly if you need to instantiate a customSubject
class.Methods in org.apache.shiro.web.mgt with parameters of type Subject Modifier and Type Method Description protected void
DefaultWebSecurityManager. beforeLogout(Subject subject)
protected void
CookieRememberMeManager. forgetIdentity(Subject subject)
Removes the 'rememberMe' cookie from the associatedWebSubject
's request/response pair.boolean
DefaultWebSessionStorageEvaluator. isSessionStorageEnabled(Subject subject)
Returnstrue
if session storage is generally available (as determined by the super class's global configuration propertyDefaultSessionStorageEvaluator.isSessionStorageEnabled()
and no request-specific override has turned off session storage,false
otherwise.protected void
CookieRememberMeManager. rememberSerializedIdentity(Subject subject, byte[] serialized)
Base64-encodes the specified serialized byte array and sets that base64-encoded String as the cookie value.protected void
DefaultWebSecurityManager. removeRequestIdentity(Subject subject)
-
Uses of Subject in org.apache.shiro.web.servlet
Methods in org.apache.shiro.web.servlet that return Subject Modifier and Type Method Description protected Subject
ShiroHttpServletRequest. getSubject()
-
Uses of Subject in org.apache.shiro.web.subject
Subinterfaces of Subject in org.apache.shiro.web.subject Modifier and Type Interface Description interface
WebSubject
AWebSubject
represents a Subject instance that was acquired as a result of an incomingServletRequest
. -
Uses of Subject in org.apache.shiro.web.subject.support
Classes in org.apache.shiro.web.subject.support that implement Subject Modifier and Type Class Description class
WebDelegatingSubject
DefaultWebSubject
implementation that additional ensures the ability to retain a servlet request/response pair to be used by internal shiro components as necessary during the request execution. -
Uses of Subject in org.apache.shiro.web.tags
Methods in org.apache.shiro.web.tags that return Subject Modifier and Type Method Description protected Subject
SecureTag. getSubject()
-


| Class | Description |
|---|---|
| BridgedIf |
Name: en1: Wi-Fi (AirPort) GUID: 00316e65-0000-4000-8000-28cfdaf2917a Dhcp:
Disabled IPAddress: <IP_ADDRESS> NetworkMask: <IP_ADDRESS> IPV6Address:
IPV6NetworkMaskPrefixLength: 0 HardwareAddress: 28:cf:da:f2:91:7a MediumType:
Ethernet Status: Up VBoxNetworkName: HostInterfaceNetworking-en1: Wi-Fi
(AirPort)
|
| BridgedIf.Builder | |
| CloneSpec |
A complete specification of a "clone" node with networking setup and the physical machine
specification.
|
| CloneSpec.Builder | |
| DeviceDetails |
Represents a specification for a device attachment.
|
| DeviceDetails.Builder | |
| HardDisk |
A representation of a hard disk in a VirtualBox VM.
|
| HardDisk.Builder | |
| IsoImage |
Represents an optical medium (DVD) in a VirtualBox VM.
|
| IsoSpec |
The information needed to create a machine from a .iso file.
|
| IsoSpec.Builder | |
| Master | |
| Master.Builder | |
| MasterSpec |
A complete specification of a "master" node, including the ISO, networking setup and the physical
machine specification.
|
| MasterSpec.Builder | |
| NatAdapter |
Represents a NAT network adapter in VirtualBox.
|
| NatAdapter.Builder | |
| NetworkAdapter |
Represents a network adapter in VirtualBox.
|
| NetworkAdapter.Builder | |
| NetworkInterfaceCard | |
| NetworkInterfaceCard.Builder | |
| NetworkSpec |
Describes the network configuration for a VirtualBox machine.
|
| NetworkSpec.Builder | |
| NodeSpec | |
| NodeSpec.Builder | |
| RedirectRule | |
| StorageController |
Represents a storage controller in a VirtualBox VM.
|
| StorageController.Builder | |
| VmSpec |
A description of a Virtual Machine in VirtualBox.
|
| VmSpec.Builder | |
| YamlImage |
Serializes to the following
|
| Enum | Description |
|---|---|
| ErrorCode |
Maps the error codes in the Virtual Box Java API into enum values.
|
| ExecutionType |
Copyright © 2009-2013 jclouds. All Rights Reserved.


Total Time:       Cook:      Level: Easy      Serves: 4-6 SERVINGS
A creamy, delicious soup the French would be proud of.
This is one of my favorite soups of all time. It is so easy. The secret to this deep, rich soup is a long slow caramelization,
the key to unlocking the mushroom's magic. This is just pure essence of mushroom.
 Add mushrooms and saute until golden, about 5-7 minutes. Season with salt
and pepper and remove 1 cup of the mushrooms, reserving them to the side.
Add shallot and garlic to the pot and saute until fragrant, 1 minute.
Add thyme and vegetable stock and bring to a simmer. Cook until mushrooms are tender, about 8-10 minutes. Using
an immersion blender, or working in batches in a regular blender, puree soup until smooth.
Return soup to pot and stir in cream and reserved mushrooms. Serve.


pyGM: Drawing¶
-
pyGM.draw.
drawBayesNet
(model, **kwargs)[source]¶ Draw a Bayesian Network (directed acyclic graph) using networkx function calls
- Args:
**kwargs
: remaining keyword arguments passed to networkx.draw()
Example:
>>> gm.drawBayesNet(model, labels={0:'0', ... } ) # keyword args passed to networkx.draw()
-
pyGM.draw.
drawFactorGraph
(model, var_color='w', factor_color=(0.2, 0.2, 0.8), **kwargs)[source]¶ Draw a factorgraph using networkx function calls
- Args:
- var_color (str, tuple): networkx color descriptor for drawing variable nodes
factor_color (str, tuple): networkx color for drawing factor nodes
var_labels (dict): variable id to label string for variable nodes
factor_labels (dict): factor id to label string for factor nodes
**kwargs
: remaining keyword arguments passed to networkx.draw()
Example:
>>> gm.drawFactorGraph(model, var_labels={0:'0', ... } ) # keyword args passed to networkx.draw()
-
pyGM.draw.
drawLimid
(model, C, D, U, **kwargs)[source]¶ Draw a limited-memory influence diagram (limid) using networkx
- Args:
**kwargs
: remaining keyword arguments passed to networkx.draw()
Example:
>>> model.drawLimid(C,D,U, var_labels={0:'0', ... } ) # keyword args passed to networkx.draw()


The Echo Chamber of Self-Satisfaction
Giving and Receiving Feedback
July 19, 2015
Being able to give and receive good feedback is probably one of the most important skills new programmers can learn. I say this, definitely not as an expert, but as a complete novice who personally finds it difficult to give and receive honest feedback.
I’ve sat down and really tried to understand why receiving feedback is so hard. It’s not that I fear any sort of criticism, I actually welcome it when necessary. I think it’s something a little bit deeper. When someone gives me feedback, good or bad, I get a moment to peer into the conscience thought of another person. I get to read their thoughts… about ME (shivers). Yes, feedback isn’t always honest or necessarily accurate some of the time, but it can still be incredibly insightful and slightly horrifying.
I think most people would classify me as an introvert; one who only speaks their mind after giving a thought a considerable amount of re-thought. So the pair programming and feedback process I’ve been introduced to so far, has been outside my comfort zone to say the least. That being said, it’s how I grow, it’s how I learn, and most importantly it’s how I improve.
 Accepting and giving feedback is just one stepping stone on the path outside. Honestly approaching yourself, and others, is one of the most freeing adventures I’ve yet to experience. Think back to your own life, is the place where you are most comfortable also the place where you can be the most honest?
Sorry, I’ve been rambling like a mad man. This all circles back to programming. One of the greatest detriments to improvement would be an echo chamber of self-satisfaction. You have to reach out and collaborate, and expose a part of yourself that’s open to change. Not all of it… it takes time to acclimate. In the process, you will learn from others and maybe come to realize a side of yourself that wasn’t previously visible. I can’t tell you how many times I’ve been amazed by reading the code of my peers, their thought process and methodology was entirely different from my own and I’m able to carry that away with me. It’s not easy, but it’s not supposed to be.


--- layout: post title: "Day 18 - Bar Brothers Workout" subtitle: "Today is the big day because today is the 18th day, and this is the third week of training week, remember that the first week was actually half a week and I only shone from Tuesday, but still, today I will check my weight and see how I have progressed along the way." date: 2019-11-08 background: '/img/posts/intro-barbrothers.png' ---
My weight did not reach 93 kg, but I managed to reach a new weight that is not marked in my weight table. Now weekend and usually weekend I gain some weight, what it takes is to be careful about what I eat.
I really hope that at the beginning of the week I will reach the weight I mentioned, it will help me a lot. Anyway, I figured out how I could change or reduce or add foods to my menu without hurting my alertness and giving my whole body what it needed, while still helping it shed as much weight as possible.
I decided to lower the carbohydrates, until today I ate rice at noon, now I'm going to eat legumes with healthier values such as peas and carrots, green beans, and of course a large vegetable salad, I have two fruits to eat during the day and drink lots of tea.
Progress is good at both the weight and the training level. In the evening I ate chicken and salad, tomorrow's day is a rest day to give the body a rest from the harsh days that have been so far.
In the morning I had some sweet food with my wife in the restaurant, but still it did not affect me so much. I did the walk I mentioned earlier.
Good night.


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We’ve all had those professors/teachers that we just don’t seem to mesh with. Recently, I had a professor with whom I could not set aside my differences. The class was about Mobile Application Development. I went into it thinking, “Wow, I’m excited! Finally, I’ll be able to create an iOS/Android application!” On the first day of the class, Mr. Barney walked in and said, “This is not a programming class. This is not an iOS development class.
Honestly, I’m a bit embarrassed to say that I didn’t know how to do this before. I’ll be creating a blog post here fairly soon on my conversion from WordPress to Jekyll for this blog, but in doing so, I’ve learned a lot of cool new things. Due to the vast number of mobile devices in circulation right now, I make it a habit to try and develop for mobile first, and then expand for the desktop user.
I’m not a programmer. Some people just have that natural itch to want to go out and code all day long, but that isn’t me. I enjoy scripting and web development, but deep, low-level programming just simply isn’t my thing. So the fact that even I was able to create a simple GUI application should tell you how easy it is. The code below might look daunting at first, but we’ll walk through it piece by piece to hopefully clear things up.
Recently, I’ve really been getting fed up with WordPress. And here’s why. WordPress dates all the way back to 2001. It was originally developed as a simple blogging platform based off of MySQL and PHP. Close to fifteen years later, it’s become a web standard and is used as the Framework/CMS for more than 20% of all the websites on the internet. And that’s exactly the problem. WordPress has exploded into a million different forks and subprojects, and has been expanded to run an equal amount of themes and plugins.
When I was 16 (yes, I’m young, okay?), I had the idea for a Tech Blog. Not that it had never been done before or anything, but it’s just that ever since the age of 9, technology is the only hobby or thing in my life that I’ve consistently held interest in. I’m not sure why I wanted to start one, but I just figured it would be a good way of tracking my progress and learning of various technologies, as well as helping others in anyway, if possible.
Let me start off right away by saying that you do not “need” an iPad. Regardless of what you do or do not have, you do not NEED an iPad.  But we’ll save that for another day. But let’s get to what you’re really interested about. I am not an Apple Fanboy (an Apple Watch for $350?!
“It’s me, not you. Okay, no. Actually, it is you. We’ve had a good run together. We’ve tried it out for more than a year, but things just aren’t working out. That’s not to say that we haven’t had some good times together, but I’m just ready to move on.” Usually, it’s safer if I keep my credit card in a different room while I’m surfing the internet late at night.
For anyone who knows me even slightly well knows that I’m a huge fan of WordPress. I use it as my go-to CMS for all of the websites I create, whether for myself or for clients. It gives me the flexibility I need in a well designed format, and it gives my clients the ability to make changes to their own websites without knowing any code. Some people will give you a giant list of plugins that you have to download when first setting up your WordPress site.
 You might be surprised. It’s not my beautiful Canon 6D. Nor is it my 24-70 F2.8 lens. It’s light, portable, and inexpensive, yet it’s the most important piece of equipment that I own: a plastic bag. Last night, we spent the evening playing with steel wool and throwing sparks into circular patterns into the air. Add a little fill flash, and some creativity, and you’ve got a fun evening planned.
About six months ago, I finally made the switch. Not because I hadn’t wanted to do it earlier, or because I suddenly became so fed up with the Windows operating system. I simply just had a little excess of money, and so I figured, “Well, this will never happen again. Might as well do it now.” Before I start this article, I simply want to throw out a disclaimer that I am not a hardcore “fanboy.” I work as a Computer Technician, and I service both Macintosh and Windows systems.


git-verify-commit - Check the GPG signature of commits
git verify-commit <commit>...
Validates the gpg signature created by git commit -S.
-v, --verbose
Print the contents of the commit object before validating it.
<commit>...
SHA-1 identifiers of Git commit objects.
Part of the git(1) suite


Some of you might know that I've bin improving the lecture notes for the computer engineering lecture (digital electronics) since April 2013.
How I've got the job
This was kind of funny. I send Prof. Dr. Asfour some notes of passages that could be improved (mainly typos). About two days later he proposed me to correct it by myself. Another day later I signed the contract. I've never signed a contract that fast.
What I did
My job was
- to correct errors (German language, statements about computer science and LaTeX),
- find parts that were outdated and update them,
- find sections that were difficult to understand and simplify them and
- to make it easier to make changes in the future (Well, I don't think Prof. Dr. Asfour thought this was my job ... but I think it's important.)
So Prof. Dr. Asfour created a SVN repository with all LaTeX sources of the latest lecture notes (which were already great!). He also sent me Emails he received from students who mentioned errors just like I did and some notes from a tutor who tried to improve the script some time ago.
Revisions
With svn checkout svn://somepath@1 working-directory
you can checkout the first revision of a SVN repository.
Total number of files and folders: find . | wc -l
- Revision 1: 1885
- Revision 30: 1488
How often did I change files (source):
svn log -qvr 1:HEAD|perl -nle 'print if /^Changed paths:/ ... /^-+$/ and /^\s/' \
| sort | uniq -c | sort -n
1 M /ti1.bib
2 M /
2 M /anhang-1.tex
2 M /anhang-3.tex
2 M /anhang-5.tex
2 M /diss-report.cls
2 M /figures/Makefile
2 M /titel.tex
2 M /zahlen_codes
3 M /vorwort.tex
7 M /einleitung.tex
8 M /Makefile
10 M /my_def.tex
11 M /sw.tex
14 M /arith.tex
14 M /skript.tex
16 M /daten.tex
19 M /sn.tex
22 M /README.txt
28 M /skript.pdf
With CodeAnalyzer over all .tex files:
| Revision 1 | Revision 30 | |
|---|---|---|
| Total files | 31 | 14 |
| Total Lines | 24,679 | 11,077 |
| Avg Line Length | 39 | 45 |
| Code Lines | 18,500 | 8,967 |
| Comment Lines | 1,124 | 871 |
| Whitespace Lines | 5,177 | 1,391 |
| Resulting PDF pages | 229 | 233 |
| Examples | 93 < | 93 |
| Images | 211 | 211 |
StackExchange
I've learned quite a lot about LaTeX while correcting the document. My questions on StackExchange might reflect that:
- xfig:
- Quotation marks: Is there any difference between \grqq/\glqq and “` / ”'?
- How to use nag? - This gave me a lot of input what I could improve
- How should I prevent images from floating between list and paragraph before
- Can I tell LaTeX to break a list?
- How can I prevent breaks in a custom environment?
- Is it possible to define an environment that might not be displayed?
- Why doesn't grep give the matching line?
And some language questions:
- Nummerierung im Text
- Gibt es ein Verb für “Ein Zeichen wird durch seine Escape-Sequenz ersetzt”?
- “Theoretische Informatik” oder “theoretische Informatik”
What I've learned
- You can open file skript.tex on line 1234 with
vim +1234 skript.tex
grep -rniI
,find
,xargs
, make and Meld are VERY useful- I like Git more than SVN (because I don't need internet to commit)
- nag package is interesting
- There seems to be no good way to create images for digital electronics which might contain LaTeX. xfig is the best I found, but it is very hard to use.
Conclusion
Working as a "Skript-HiWi" is easy work, but more time consuming than you might think. Even if you know how to work with LaTeX. Rebuilding a big document takes some time.
I was astonished that there were some topics which I didn't understand yet. After I've prepared for the exam, I thought I knew everything in the lecture notes. Obviously, this was not the case (or I forgot how to do division with twos complement meanwhile).


Swag dolor assumenda cold-pressed, selvage mixtape vero pour-over sriracha poutine. Nisi accusamus chartreuse, craft beer franzen chia kale chips hoodie hella you probably haven't heard of them locavore offal do minim. Authentic nostrud occaecat stumptown chillwave. Flannel mlkshk hashtag everyday carry exercitation, culpa kinfolk quinoa kombucha officia YOLO irony. Nulla jean shorts adipisicing, echo park dolore anim brunch you probably haven't heard of them banh mi gentrify narwhal. Aliquip paleo sriracha shabby chic id bushwick. Cillum organic tilde retro banh mi stumptown.
Swag dolor assumenda cold-pressed, selvage mixtape vero pour-over sriracha poutine. Nisi accusamus chartreuse, craft beer franzen chia kale chips hoodie hella you probably haven't heard of them locavore offal do minim. Authentic nostrud occaecat stumptown chillwave. Flannel mlkshk hashtag everyday carry exercitation, culpa kinfolk quinoa kombucha officia YOLO irony. Nulla jean shorts adipisicing, echo park dolore anim brunch you probably haven't heard of them banh mi gentrify narwhal. Aliquip paleo sriracha shabby chic id bushwick. Cillum organic tilde retro banh mi stumptown.
End-user documentation for the D3D9 Client.
open_in_newOpen
Content creator documentation for integration on D3D9.
open_in_newOpen
End-user documentation for the D3D9 Client.
open_in_newOpen (HDR)
open_in_newOpen (Stable)
Content creator documentation for addon integration in the D3D9 Client.
open_in_newOpen (HDR)
open_in_newOpen (Stable)
API Reference for the D3D9 Client.
open_in_newOpen
Download the D3D9 for Orbiter 2010, recreating the graphics from Orbiter while adding new features such as reflections, specular maps, etc.
Download the D3D9 Client for Orbiter 2016. This new version includes the support for Orbiter's terrain feature, advanced rendering of planetary bodies and atmospheres, and HDR rendering.


After you enter your content, highlight the text you want to style and select the options you set in the style editor in the "styles" drop down box. Want to


2004-2008 : IIT Kanpur ( Bachelors in Biosciences & Bio-Enginnering ), Made lifelong friends, A time I can live thousands time over.
2008 (July)-2009(November) : Worked as Credit Risk Analyst with Global Analytics ( Chennai ). Good times and Bad times, got curious about using Machine Learning to solve business problems and then decided to leave.  Joining Indian Statistical Institute, Kolkata would be the next step.
2010 (July)-2012(March) : ISI Kolkata (Masters in Operations Research) Fell in love with statistics and everything ML. Met one of the most amazing people and more friends , yay!
2011 (?) - 2013 (June) : Learned a valuable lesson : you can tell the world that your business failed because of failure to raise money, however you most certainly lacked necessary skills to make it successful. Much needed humbling experience ( forced to shutdown two companies ! )
2013 (July) : Launched Edvancer with Aatash Shah , online courses on Machine Learning with SAS (We have long since moved on to R, Python and other open source tools)
2014 : Launched Courses with R. SAS demand was going down fast by this time, Started taking ML, DL sessions for various corporate clients. eClerx, Mumbai was our first client
2015 : Launched our first course using Python
2015-2018 (August) : Various online courses using R, Python, SQL, Tableau, Hadoop have been added to catalog by now( ML, DL, Big data, NLP with both live online classes as well as self paced options)
2018 (August) : Finally relented and launched classroom sessions @Hyderabad with a 6 month long comprehensive program ( covering ML, DL, Big data, SQL, Tableau)
2019 (July) : Launched classroom programs @Mumbai & @Delhi after overwhelming response in Hyderabad. We have completed 100+ sessions on ML/DL/Stats topics across multiple companies (Deloitte, EY, Genpact, GE, Colpal, PWC, HDFC have been one of the frequent clients that I keep on visiting)
Future : Lets See


In this project reinforcement learning techniques are applied for a self-driving agent in a simplified world to aid it in effectively reaching its destinations in the allotted time.
Firstly, I have constructed a very basic driving implementation to investigate the environment the agent operates in.
Once the agent is successful at operating within the environment, then each possible state the agent can be in is identified when considering such things as traffic lights and oncoming traffic at each intersection.
With states identified, a Q-Learning algorithm is implemented for the self-driving agent to guide the agent towards its destination within the allotted time.
Finally, the Q-Learning algorithm is improved to find the best configuration of learning and exploration factors to ensure the self-driving agent is reaching its destinations with consistently positive results.
Visualized every step using PYGAME library.
Environment in which the Smart Cab operates in:
It operates in an ideal, grid-like city with roads going in the North-South and East-West directions. Other vehicles will certainly be present on the road. At each intersection there is a traffic light that either allows traffic in the North-South direction or the East-West direction. The traffice light rules followed are as :
On a green light, a left turn is permitted if there is no oncoming traffic making a right turn or coming straight through the intersection.
On a red light, a right turn is permitted if no oncoming traffic is approaching from your left through the intersection.
Driving Agent:
The smart cab is assigned a route plab based on the starting location and destination. While moving, the smart cab can determine the state of the traffic light for its direction of movement, and whether there is a vehicle at the intersection for each of the oncoming directions. For each action, the smartcab may either idle at the intersection, or drive to the next intersection to the left, right, or ahead of it. Finally, each trip has a time to reach the destination which decreases for each action taken (the passengers want to get there quickly). If the allotted time becomes zero before reaching the destination, the trip has failed.
Rewards:
The smartcab will receive positive or negative rewards based on the action it as taken. Expectedly, the smartcab will receive a small positive reward when making a good action, and a varying amount of negative reward dependent on the severity of the traffic violation it would have committed. Based on the rewards and penalties the smartcab receives, the self-driving agent implementation should learn an optimal policy for driving on the city roads while obeying traffic rules, avoiding accidents, and reaching passengers’ destinations in the allotted time.
Goal:
The goal of the smart cab is to reach the destination with maximum reliability and security as well.
Basic Driving Agent:
First, I have implemented a basic driving agent and visualized the results after performing simulation.
Certain Observations made after implementing a basic driving agent:
Following the '10-Trial Rolling relative frequency of bad actions' plot, around 36%-45% of the times, the driving agent is making the bad decisions. Within this, around 20% relative requency is for accidents & violations with around 5% relative frequency for major accidents and around 4% for minor accidents.
Given the agent is driving randomly,the '10-Trial Rolling Rate of Reliability' plot, shows the rate of reliability between 0%-18% ,that is, the agent reaches its correct destination around 0%-18% of the times. Thus, since the agent is taking random actions everytime, the reliability is supposed to be low only.
Since around 36%-45% of the times, the driving agent is taking bad decisions, thus, the '10-Trial Rolling Average Reward per Action' plot clearly shows that the actions are getting negative rewards. Also, as the number of trials increases, the rewards for each action are getting more negative. Hence, it is clear that the rewards have been penalized heavily.
Since, everytime the agent is taking random actions, it's not learning from the previous trials, thus the outcome of results does not change significantly.
The Safety & Reliability Ratings obtained are 'F' each. Hence it is obvious that SmartCab is not considered as both safe and reliable for its passengers. This is because the agent is taking random actions everytime, it is not learning from the trials.
Creating a Q-Learning Driving Agent:
I have created an optimized Q-learning driving agent by defining a set of states that the agent can occupy in the environment. Depending on the input, sensory data, and additional variables available to the driving agent, a set of states can be defined for the agent so that it can eventually learn what action it should take when occupying a state. The condition of 'if state then action' for each state is called a policy, and is ultimately what the driving agent is expected to learn.
Features that are most relevant for learing safety and reliability:
Though all the available features {'waypoint', 'light' , 'left', 'right', 'oncoming', ' deadline' } play an important role in determining the safety and reliability of SmartCab, but if more 'reliability'is needed, then 'deadline' and 'waypoint' should be given more importance than other features because these features will determine the direction leading to the destination without taking care of safety and hence 'safety' would be inefficient and for 'safety', I think, 'oncoming', 'left', 'right' would be more important because these features will determine taking a right decision at every step without making violations and ultimately, reaching the destination.
Also, according to me, the most relevent ones are waypoint, light, left and oncoming. The 'deadline' feature is not that much appropriate because knowing the number of steps left to reach the destination would not help much in improving the Safety and Reliability Ratings.
Also, the 'right' feature is not that important because the intended direction of travel for a vehicle to the Smartcab's right would not help in much learning from the trials. While on the other hand, the 'left' and 'oncoming' features are important because if a vehicle is present to the smartcab's left,then its intended direction of travel can help the smartcab learn to take certain actions in different directions.
Q-Learning without Optimization :
Certain observations made after implementing a Q-Learning Algorithm (without optimization):
The initial Rate of reliability, initial reward per action and the initial relative frequency of bad decisions are alomst same (although not completely equal) for both the basic driving agent and the defaukt Q-Learning agent.
Around 20 trials are required before testing. Given the default epsilon-tolerance (0.05) and since the decay function is linear (epsilon = epsilon - 0.05; decay of 0.05) , it takes around 20 trials in order to converge to epsilon-tolerance.
The 'Parameter-Value Vs Trial' plot clearly shows that the exploration factor (blue line in the plot) drops linearly from 1.0(initial epsilon value) to 0.0(~0.05(tolerance)) {decay value is 0.05} . Mathematically, 0.05 (decay factor) * 20 (No of trials) = 1.0. Hence, it takes 20 trials to converge to tolerance before testing.
The '10-Trial Rolling relative frequency of bad actions' plot clearly shows that the number of bad actions decreases as the number of trials increases( from 30% to 10%). Also, the '10-Trial Rolling Average Reward per Action' plot shows the average reward increases per action. This is because, now the agent is learning from the trials and thus, the number of bad decisions made are lesse and hence the average reward per action has also increased.
The safety and Reliabilty Ratings have now been improved to 'D' and 'B' respectively.
Q-Learning Algorithm with Optimization:
Certain observations made after implementing a Q-Learning Algorithm (with optimization):
I used the 'cosine' decaying function {self.epsilon = math.cos(self.t * 0.007) ; where self.t: number of trials}.
Approximately, 225 training trials are needed for the agent before beginning testing.
I used the epsilon-tolerance : 0.01 so that the agent can perform a sufficient number of trials before the testing starts and alpha (learning rate) as 0.65 ( have not taken either very big value or very small value ). As the alpha determines the magnitude of step that is taken towards reaching the deadline; thus, if a very small value is chosen, it may take a long time to reach the deadline or if a large value is taken, though it'll learn at a faster rate but then there will be a high of probability of oscillating continously around the final destination only. Hence, chose alpha = 0.65.
A lot of improvement has been seen ; rate of reliability has increased to almost 100%. Also, the relative frequency of bad decisions taken by the agent has ultimately converged to almost 0.0.
The Q-Learner results clearly show that the driving agent has successfully learned an appropriate policy.
Both the Safety and Reliability ratings of the SmartCab obtained are 'A+' which is satisfactory.
Defining an Optimal Policy:
In the given environment, the optimal policy would be to reach the destination safely while avoiding any violations and accidents (No clashes with other vehicles)


"BANI "built. (1.) 1 Chronicles 6:46. (2.) One of David’s thirty-seven warriors, a Gadite (2 Samuel 23:36). (3.) Ezra 2:10; 10:29,34,38. (4.) A Levite who was prominent in the reforms on the return from Babylon(Nehemiah 8:7; 9:4,5). His son Rehum took part in rebuilding the wall of Jerusalem (Nehemiah 3:17).
"BANI "built. (1.) 1 Chronicles 6:46. (2.) One of David’s thirty-seven warriors, a Gadite (2 Samuel 23:36). (3.) Ezra 2:10; 10:29,34,38. (4.) A Levite who was prominent in the reforms on the return from Babylon(Nehemiah 8:7; 9:4,5). His son Rehum took part in rebuilding the wall of Jerusalem (Nehemiah 3:17).


{% extends 'base.html' %} {% block extra_css %} {% endblock %} {% block content %}
| ID | Created | Players | Actions |
|---|---|---|---|
You have not created any games |
| ID | Created | Players | Actions |
|---|---|---|---|
Noone else has created any games |


FRIENDS - THE ONE WHERE RACHEL GOES BACK TO WORK
Teleplay by: Peter Tibbals
Story by: Judd Rubin
Directed by: Gary Halvorson
[Scene: Chandler and Monica's]
Chandler: Hey!
Monica: Good morning, Tiger!
You're gonna get me good and pregnant.
Chandler: I've got nowhere to go this morning. I'm unemployed!
Monica: Well, I just lost my erection.
Chandler: I mean, what am I supposed to do with myself?
Monica: You're supposed to find your passion in life. You can be whatever you wanna be now. It's exciting.
Chandler: But it's all so overwhelming. I don't know where to start.
Monica: Hey, wait a second. I can help you with this. You just need to be organized. We can make a list of your qualifications,
and categorize jobs by industry. There could be folders and files!
Chandler: Hey! This is where your hyper-organized-pain-in-the-ass stuff pays off!
Monica: I know!!! My erection is back!
[Opening credits]
[Scene: Central Perk]
Joey: (To Gunther who comes over with coffee and a muffin) Thank you!
Phoebe: Joey, can I have a sip of your coffee and a bite of your muffin?
Joey: Okay.
Phoebe Thank you. (Pours his coffee in a thermos and puts his muffin in her purse.) Thank you!
Joey:: Pheebs, have you ever been bitten by a hungry Italian?
Phoebe: I'm sorry, it's just, I'm a little short on cash.
Joey: If you want I could loan you some money?
Phoebe: Oh no, no, no. I learned never to borrow money from friends. No, that's why Richard Dreyfuss and I don't speak anymore.
Joey: Oh, hey, how about this? Wanna be an extra on my show?
Phoebe: You could do that?
Joey: Yeah, yeah. The pay is pretty good and you could do it for as long as you need.

Joey: Okay, now. I gotta tell you, being on TV isn't as glamorous and exciting as you think.
Phoebe: Oh, really?
Joey: No it is awesome!
(Rachel and Ross enter with Emma)
Ross: Hi guys.
All: (Adlib hellos)
Phoebe: Wow! Hey, why are you all dressed up?
Ross: Rachel and I are bringing Emma to Ralph Lauren today to introduce her to everyone. Doesn't she look cute?
Joey: She sure does. Why does she have a pink bow taped to her head?
Rachel: Well, because if one more person says "what a cute little boy" I'm gonna whip them with a car antenna!
Ross: I think she's gonna be the hit of the office, huh? She's gonna be hotter than peasant blouses and A-line skirts.
Can I get a blue bow?
[Scene: Chandler and Monica's]
Monica: (Flipping through bunch of folders as Chandler enters from bathroom) Okay, I have looked through a bunch of career guides,
photocopied and highlighted key passages and put them into alphabetical folders so you can make an informed decision.
Chandler: How long was I in there?
Monica: Okay. Let us start with the A's. Advertising.
Chandler: Wait. Advertising! That's a great idea!
Monica: Don't you want to look through the rest?
Chandler: I don't think I have to hear the rest. Advertising makes perfect sense. Sorry you had to waste all this time, though.
Monica: You call eight hours alone with my label maker wasted time? Ooh, now I get to use my shredder!
Chandler: I mean, I can write slogans. I mean, how hard can it be, right? "Cheese. It's milk that you chew." "Crackers. Because your
cheese needs a buddy." "A grape. Because who can get a water melon in your mouth?"
Monica: I got one. "Socks. Because your family's feet deserve the best."
Chandler: Honey? Leave it to the pros.
Monica: I actually know someone in advertising. I grew up with this guy who is a vice president at a big agency.
Maybe I can get him to meet you? Give me the phone.
Chandler: "The phone. Bringing you closer to people...who have phones."
Monica: "Marriage. It's not for everybody."
[Scene: Ralph Lauren]
Rachel: That went well. Almost everybody knew that she was a girl.
Ross: Yeah, after you punched that one guy who got it wrong, word spread.
Rachel: I'm just gonna go in my office and pick up some stuff .

Ross: Good one, Rach.
Guy: I'm Gavin Mitchell, the person who's taken over your job.
Rachel: Excuse me?
Gavin: Oh, your baby's so cute. Why did you put a pink bow on a boy?
[Scene: DOOL set]
Phoebe: Joey, look at me! I'm a nurse!
Joey: Yes you are. I think it may be time for my sponge bath. Sorry, I'm just so used to hitting on the extras.
So, are you excited about your scene?
Phoebe: Yeah! But I'm a little shaky and nervous.
Joey: Oh, relax. Don't be. You'll be fine, you'll be fine. They'll probably just make you stand in the back.
Director: Okay, okay, okay! (To Phoebe) You. Here, come here, here.
You're gonna move on "action!" You're gonna walk over to the operating table.  Don't walk too fast! But don't doddle.
Phoebe: Okay, now. What?
Director: And...Action!!
(Phoebe starts walking toward the operating table. Her hands are shaking like crazy, causing the tray to rattle.)
Director: Cut! Cut!
Phoebe: I'm sorry, I'm sorry, I'm sorry. I'm just a little nervous.
Director: Well, don't be.
Phoebe: Okay, that helps.
Director: And...Action!
(Phoebe starts walking towards the table again. She's even more shaky than before and almost drops the tray.)
Director: Cut!
Joey: Hey, you know what? Don't worry, Pheebs! It usually takes me three takes too! (Off the director's look) Alright, eight.
Director: And...Action!
(Ok, here we go again. Phoebe starts walking toward the operating table, but stops every time the tray starts to rattle. She eventually drops it on the floor, but finishes the scene anyway by putting the now non-existent tray on the table.)
[Scene: Central Perk]
Interviewer: So, do you have any other question about advertising?
Chandler: No, no. But let me show you what I can do. "Bagels and donuts. Round food for every mood."
Interviewer: Monica warned me you might do that. I actually think we might have something for you at the agency.
Chandler: Really? That's great!
Interviewer: It's an unpaid internship.
Chandler: It's funny. When you said "unpaid" it sounded like you said "unpaid."
Interviewer: Come on now. Monica has a good job. And it's not like you have a family to support.
Chandler: Actually, we're trying, and I don't think Monica's gonna wanna postpone it.
Actually, she's probably at home naked right now. I tend to keep talking until somebody stops me. I can just picture her on the bed right now.
Interviewer: Stop!
Interviewer: Okay.
[Scene: Ralph Lauren]
Rachel: Wait a minute! What do you mean, you're taking over my job?
Gavin: Well, while you were on your baby vacation I was doing your job.
Rachel: A vacation? My idea of a vacation does not involve something sucking on my nipples until they are raw.
Gavin: Clearly you've never been to Sandles Paradise Island.
Rachel: Alright! Don't get too comfortable there, because I'm back in two weeks! And I want everything back to the way it was.
I can't say that I care too much for the way you've rearranged my office.

Rachel: Excuse me?
Ross: Rach we have a code brown situation.
Rachel: Can you please, please take care of it for me?
Ross: Alright, but you have to do one sometime.
Rachel: Let me just get this straight! So I go have a baby and they send some guy in to do my job?
Gavin: Well, there was talk of shutting down Ralph Lauren all together.
Rachel: That's right. You're very cheeky for a temp.
Gavin: I'm not a temp. I was transferred here from another department.
Rachel: Oh yeah, what department was that? The Jerk department?
Gavin: Oh, they didn't tell me about your quick wit.
Rachel: Did they mention that I'm rubber and you're glue?
Mr. Zelner: (Enters) Gavin, Ralph loved your ideas.
Rachel: Oh, hi Mr. Zelner.
Mr. Zelner: Rachel, I see you've met Gavin. I must say, when you left us we weren't sure what we were gonna do. But then,
Gavin to the rescue. Super Gavin!
Rachel: That's great. So now, Super Gavin, when I come back where are you planning on flying off to?
Gavin: Well, that's up to Mr. Zelner. I'm sure he will make the right decision.
Rachel: (To herself) Oh, wow. Super ass-kissing power.
Mr. Zelner: Incidentally, when are you coming back?
Rachel: Today.
Gavin: You said two weeks.
Rachel: No, I said today! See, for a superhero, not so much with the listening.
[Scene: DOOL set]
Joey: Hey.
Phoebe: Hey. So, what did he say?

Phoebe: I'm sorry, I can't do this. I'm not an actor.
Joey: That's right, you're not.  You're Nurse With Tray.
Phoebe: Joey.
Joey: No, no. Nurse With Tray doesn't know Joey, she doesn't have time for friends. She gets in that operating room and she
carries that tray to the doctor, because if she doesn't, people die!
Phoebe: Who dies?
Joey: (Points to man on operating table) Man With Eye Patch!
Phoebe: Yes, doctor.
Joey: Okay.
Director: Okay, let's try this one more time.
Phoebe: Hang in there, Man With Eye Patch, your tray is coming!
Director: And...Action!
Phoebe: (Does the scene) Yes, I did it!!! I nailed it!!! Yay! What's next?
Director: The rest of the scene.
Phoebe: Okay, from the top, people!
[Scene: Ralph Lauren]
Rachel: Listen. Sudden change of plans. My maternity leave just ended.
Ross: What? No, that's illegal. I'm gonna have the labor department down her so fast they won't even...
Rachel: Alright, alright. Calm down Norma Rae. They didn't actually say that. I'm just afraid if I don't come back right now
this guy's gonna try to squeeze me out.
Ross: What about Emma? We don't have a nanny.
Rachel: I know. You know, we're just gonna have to figure out a plan tonight. Can you please just take care of her for today?
Ross: Absolutely. Just give me your breast and we'll be on our way.
Rachel: Come on, I don't know what else to do.
Ross: Fine, fine. (To Emma) We'll have fun, won't we? Yes, we will, yes we will. (Gives her a kiss, and the pink bow tapes itself to his head.)
Rachel: Ross?
Ross: Huh?
Rachel: You're pretty.
[Scene: Chandler and Monica's]
Chandler: Whatever I decide to do, I'm gonna be starting a career from scratch. It's gonna be a while before I make a living at it.
Maybe now is not the right time to be starting a family.
Ross: So you have to tell Monica you don't want to have a baby right now?
Chandler: Yeah.
Ross: (Hugs Chandler) Good-bye.
Monica: (Enters) Okay, it's baby time. Pants off Bing. (Sees Ross) Didn't see you there Geller.
Chandler: Yeah, Ross is here so...
Ross: Yeah, and I was really hoping that I could hang out. What do you feel like doing?
Monica: We're gonna have sex.
Ross: I don't feel like having sex. Maybe we can watch a movie or something.
Monica: Let me put it this way.
Ross: Pants off Bing!
[Scene: Ralph Lauren]
Rachel: Alright.
Gavin: Well, I've changed your screensaver from that picture of *Nsync.
Rachel: Hey, they were popular when I left!
Gavin: Also, I've just been working on this big presentation for tomorrow.
Rachel: Well, I should be involved in that, so why don't you get me up to speed?
Gavin: That's gonna take weeks. Why don't just let me take care of the presentation?
Rachel: Oh, no no no no. I see what you're doing here, alright, listen, this is my job buddy.
Gavin: Fine.
Rachel: (Sits down in her chair) Oh god. You've totally messed with the back support of my chair. How do you fix this?
Gavin: Hey, you've been here five years, you figure it out.
Rachel: Fine, I will. (Pushes that button thing on the chair that makes it "collapse") Alright, fill me in!
[Scene: Chandler and Monica's]
Ross: Hey.
Chandler: Hey.
Ross: You have a blue tie that would go with this? Emma spit up on mine.
Chandler: Oh, yeah. But you have to give it back if I get a job. Of course, by that time in the future ties will be obsolete
and we'll all be wearing silver jump suits.
Monica: (Enters from the bedroom) Hi, good morning lover. I gotta say after last night, I'm a little weak in the knees.
Ross: Here's an idea. You walk into a room...take a quick scan!
Monica: (To Ross) Sorry. (To Chandler) But I kind of have this feeling that we may have made a baby last night.
Chandler: Oh god, I have to tell you something. You're not pregnant.
Monica: What are you talking about?
Chandler: That thing that I have to do to make a baby. I faked it.
Monica: What!? You faked it?
Ross: You know what? I don't need a tie. I mean, it's better, open collar. You know, it's more casual (He leaves.)
[Scene: DOOL set]
Phoebe: Joey, listen. I can't do this, it doesn't make any sense. Yesterday I was a nurse, and today I'm a waitress at a cafe?
Joey: Oh, sometimes we use the same extras for different parts. It's okay.
Phoebe: Well, it's not okay, because I gave a very memorable performance as the nurse. And now suddenly I'm the waitress?
That's gonna confuse my fans.

Phoebe: Uhuh. Because I'm a single mother, supporting my two children.
Joey: Nice.
Phoebe: Wait a minute. Dr. Drake Ramoray and I work at the same hospital. Wouldn't I come over to say hi to him?
Joey: No, no. See, you and Drake are having a fight.
Phoebe: About what?
Joey: He slept with you and then never called you.
Phoebe: And I just wanted a new daddy for Davy and Becky.
Director: Okay, okay, from the top. And...Action!
Man: So, I'm surprised you agreed to have lunch with me.
Joey/Drake: I'm surprised to, but yet here I am.
(Phoebe walks by and slaps him.)
Director: Cut, cut! What are you doing!?
Phoebe: Yeah, well, I'm very angry at him, because he slept with me and never called me back.
Extra: Me too!
Another extra: Me too!
Joey: Oh, calm down! She means on the show! We need some new extras around here!
[Scene: Chandler and Monica's]
Gavin: (Enters) Wow, you're here already.
Rachel: Yes. Emma and I came in a little early to do research on the presentation. I actually made a few changes, but
I think I'm caught up on everything. So ask me anything!
Gavin: How do you fix the chair?
Rachel: Except that! (Mr. Zelner enters) Oh, hello, Mr. Zelner. We're all ready for our presentation this afternoon.
Mr. Zelner: Good, because it's in ten minutes.
Rachel: What? I can't do that! I have the baby, and Ross is not gonna pick her up for another hour.
Mr. Zelner: Well, then Gavin can give the presentation, okay, we have to do it now. Ralph needs to leave early today. He's going
helicopter shopping.
Rachel: Well, there you go. You win, you win. You get to do the presentation, you'll knock 'em dead,
no one will ever remember that I worked here, and then Ralph will buy his helicopter, and Super Gavin will just fly right along side of him!
Gavin: You can do the presentation.
Rachel: No, I can't, I have a baby.
Gavin: I'll watch her.
Rachel: Why would you do that?

Rachel: That's really nice.
Gavin: I should tell you that crying women make me very uncomfortable.
 (Starts crying) I'm sorry, I'm sorry, I'm sorry. Thank you, thank you.
Gavin: I'm really fine, don't worry, I'm great with children. (To Emma) Gavin Mitchell. Pleased to meet you.
[Scene: DOOL set]
Joey: Please don't fire my friend. Just let me talk to her.
Director: Okay, okay. But this is her last chance.
Joey: Thank you, thank you. How about I do something for you?
Joey: Hey, listen Pheebs. I was just talking to the director, and he was thinking, maybe this time you don't hit Drake,
you just wait on the tables?
Phoebe: I can't do that. I'm an actor. I have a process.
 You have a table with a hole in it.
Phoebe: Wait a minute, I see what's happening here. You're threatened.
Joey: What?
Phoebe: Yeah, I'm so good in this scene that I'm stealing focus
from you. Well, rise to the challenge Tribianni 'cause I just raised the bar. Come join me up here!
Joey: (To the director) Yeah, you can fire her, but I would call security, she won't go easy.
[Scene: Chandler and Monica's]
Monica: You faked it? You couldn't have faked it!
Chandler: Oh yes you can. You just make the faces and the noises.
Monica: Guys can fake it? Unbelievable. The one thing that's ours! Besides, why would you fake it when we're trying to have a baby?
Chandler: That's actually why. Look, I'm starting a whole new career now, and I'm not saying that I don't want to have a baby,
I'm just saying maybe we could wait a little while.
Monica: Like a month?
Chandler: Or a year?
Monica: Really, you want to wait a year?
Chandler: It could be less than a year. I mean, you've heard my stuff. "Pants. Like shorts, but longer." (Pause)
It'll probably be more than a year.
Monica: I really wanna have a baby.
Chandler: Yeah, me too. Look, I'll just get my old job back.
Monica: No, I want you to have a job that you love. Not statistical analysis and data reconfiguration.
Chandler: I quit and you learn what I do?
Monica: It's just, I think, there's never gonna be a right time to have a baby.

There's always gonna be a reason not to do this, but I think once the baby comes, forget about all those reasons.
Chandler: I guess. It's always gonna be scary when we have a baby.
Monica: It's gonna be really scary. I mean, god. When we have a baby, there's gonna be so much that we're not able to control.
I mean, the apartment's gonna be a mess, I won't have time to clean it. What if the baby gets into the ribbon drawer?
Messes up all the ribbons?! What if there's no room for a ribbon drawer, because the baby's stuff takes up all the space!?
Where will all the ribbons go!?!
Chandler: Should we go make a baby right now before you change your mind?
Monica: Yes, please!
Chandler: Oh, and I promise, I will not fake it this time.
Monica: I wish I could say the same.
[Closing credits]
[Scene: DOOL set]
Joey/Drake: I know you botched that operation on purpose. I can't prove it yet, but when I do, you'll be going to jail for murder.
I don't care if you are my brother.
Man: I'm not your brother.
Phoebe/Waitress: What about my children Drake?! Huh!? (She starts running around on the set with a security guy chasing her) No!! No!! No! God!


Sravanthi Cinemass has acquired the RAJA VIJAYA SIMHA cinema rights for Telugu language.
RAJA VIJAYA SIMHA is a fantasy action film written & directed by debutante Director Sakthivel Perumalsamy. It stars Bobby Simha, Kalaiyarasan & Reshmi Menon. Produced by Sravanthi Cinemass, the film has Cinematography by Ravindranath Guru and Music composed by Achu Rajamani and Telugu Dialogues & Lyrics by Babu, Editing & VFX by Durgash. RAJA VIJAYA SIMHA is a Ultimate Revenge saga spanning across three generations. The film also uses Motion capture technology in a five minute opening sequence.
Cast
Bobby Simha, Kalaiyarasan, Reshmi Menon, Charle, Manobala, Appukutty, Kaali Venkat, R. S. Shivaji, Guru Lukman, Karthik Yogi, Sandra Amy, Gajaraj, Oviyar Veera Santhanam, Anthony Daasan, B. H. Tharun Kumar.


Character: Actions
Actions table
We can define actions and their consequences (effects)
for each character. To add a new action
you click on .
Availables actions ar
-
Normal actions: with Examine,
Use and Talk to (usually used
to launch a conversation).
-
Custom action: Custom
action can be adapted to fit different requirements. Once selected, a
dialog will ask us for a name for the action, (e.g.: buy, open, throw,
etc. ). A personalize appearence can be given to this actions in the Personalization
tab
Each action is defined in the same way,
beyond some effects
that will be explained later and some conditions that appear in the top
table:
-
Needs
get to: if it is necessary to reach and item
to perform an action. We can set the minimum distance between the item
and the main character necessary to perform the action. This condition
is only used in third person adventures.
-
Conditions:
This defines the conditions which must be checked before the action is
performed. If the conditions are not met, nothing will happen. More
information about conditions.
Description and effects
Each action has a Full description for
the action and Effects.
In the first, we can add all the information we want about the action.
In the second, clicking on
we can add Effects for the
action. More about effects.
Also, selecting Active when conditions
are not and then clicking on Edit
not-effects we can define the effects that will be triggered when
we perform the action but the conditions defined in top table are not
met.


On Thursday, along with Jeremy and Leo, I'll be running an OCaml Hands-on Session at the FPDays conference. Below are some prep instructions for attendees.
If you're starting from scratch, installation can take some time so it's best to get as much done in advance as possible. You'll need OPAM (the package manager), OCaml 4.01 (available through OPAM) and a few libraries before Thursday. If you have any issues, please contact Amir.
OPAM: Follow the instructions for your platform at http://opam.ocaml.org/doc/Quick_Install.html.
OPAM requires OCaml so hopefully the relevant dependencies will kick in and
you'll get OCaml too (most likely version 3.12). You can get a cup of
coffee while you wait. After installation, run opam init
to initialise OPAM.
OCaml 4.01: We actually need the latest version of OCaml but OPAM makes this easy. Just run the following (and get more coffee):
$ opam update
$ opam switch 4.01.0
$ eval `opam config env`
libffi
, pcre
and pkg-config
. This will depend on
your platform so on a Mac with homebrew I would do
brew install libffi pcre pkg-config
or on Debian or Ubuntu
apt-get install libffi-dev
. After this, two OCaml packages it's worth
installing in advance are core
and js_of_ocaml
so simply run:$ opam install core js_of_ocaml
OPAM will take care of the dependencies and the rest we can get on the day!


MADADAYO A film review by Rachel Gordon Copyright 2001 filmcritic.com filmcritic.com
It seems only fitting that Akira Kurosawa's last film (which he wrote and directed) be an homage to teaching. After all, he inspired many of today's respected directors and is taught in film schools internationally. Madadayo is a sweetly overlong portrayal of an interconnected community whose center is a beloved professor.
Madadayo opens with a Professor (Tatsuo Matsumura) announcing to his class that after 30 years of teaching, he is now choosing to write in retirement. The young sing his praises and previous alumni move the Professor and his wife into a new home. The pair is as hospitable to everyone as if they were children returning from college.
His students visit him constantly, trying to repay all his work with whatever token of comfort they can. Though he is still called Professor and spins new tales, the relations no longer have the condescending quality of talking down to a pupil. The generation lines are blurred more as the youths build a house for the couple when their new home has been destroyed by air raids in the second World War.
With the perfect mix of humility, humor, and acceptance, it's easy to understand the Professor's popularity. He is idolized even when most vulnerable. It is also accepted when his alumni refuse to allow him to wallow in self-pity. As the standard of living decreases, the Professor accepts his pupils as equals that much more, for each chips in to help.
Every year after his retirement, a Maadh Kai (translation: Are you ready?) party is thrown in the Professor's honor. They shout verses back and forth to jokingly confirm to the entire community that he isn't ready to die yet. The tiresome length of these scenes is somewhat abated by the contagious laughter and various toasts as to the nature of the Professor's lessons.
The Professor is never shown in front of a class again, so his pupil's words are the strongest indications of the type of mentor he was. Added to this is that the only time the young peers are shown communicating without the Professor around is when they are talking about him. Matsumura carries the nucleus of the film well, with his subtle sarcasm and silent revelry. The script is also intelligent enough to display the wonderful impact he has had on others' lives without having to bear witness to a school lecture.
As kind and humbling as Madadayo is, the plot becomes monotonous. Each section simply presents something going wrong with the Professor's life, and his students then resolve the issue. One unending series of scenes revolves around the revered older gentleman losing emotional control over a cat that disappears. The beauty of his visions about the missed feline is intoxicating, as are the verses spoken, but the effect wears off due to the long interval.
As with other foreign subtitled films, some of the culture-based jokes won't have the same strength when translated, leaving the viewer at a loss to understand why the characters are laughing. The unique collaboration between age groups is pleasant but it's difficult to accept that these lads risk setting their mentor on the correct emotional track only once.
Impeccably shot by Takao Saito, who was also the cinematographer for Kurosawa's Ran and Dreams, it can be difficult to concentrate on what is being said. From lush gardens to stark devastation after World War II, cultural details seem to flow past the screen.
Similar to Stand and Deliver and other "perfect teacher" movies, Madadayo is a feel-good experience worth a rental, if only because the central character is flawed, and therefore a more interesting study. The humor is contributes unexpectedly poignant satire, which is rarely found in the peers of this genre. However, for a truly brilliant Kurosawa experience, check out his earlier work first.

Visit filmcritic.com on the Web at http://www.filmcritic.com
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


Extensions |
||||
|
There are some megawidgets written in Tk and Itk whose handling is exact as the normal widgets. You can always use these mega widgets. If there is a tcl file (start-tk) (eval-wish "source {megaspinbox.tcl}") (define mbox (tk 'create-widget 'megaspinbox)) Because it is frequently used, the megawidget |


Watch: Manipur’s Rex Singh grabs eight wickets on opening day of Ranji Trophy’s new season
by Scroll StaffManipur fast bowler Rex Singh is once again making heads turn. On Monday, the left-arm pacer starred with the ball during Manipur’s match against Mizoram on the opening day of 2019-’20 Ranji Trophy season at Jadavpur University Ground in Salt Lake on Monday.
Batting first in the Plate Group match, Mizoram were bundled out for just 65 in just under an hour with Rex Singh being the wrecker-in-chief. The 19-year-old registered figures of 8-4-22-8 and five of the batsmen who he dismissed, were out for nought.
Watch his wickets here:
The youngster later scored an unbeaten fifty as Manipur finished the day with 255/7 at stumps, accumulating a lead of 190 runs.
Singh first grabbed the headlines last year when he claimed 10 wickets during a Cooch Behar Trophy game against Bihar in December. His heroics earned him a promotion to the U-19 Indian cricket team, becoming the first player from his state to be drafted in the national setup.
Earlier last month, he even made his Twenty20 debut for Manipur during the 2019-’20 Syed Mushtaq Ali Trophy against Rajasthan.
Born in Imphal, Singh started playing cricket with tennis ball in his backyard. As reported by Sportstar, he was initially interested in football and taekwondo, but switched to cricket on the recommendation of his coach Ph Rohendro Singh.
“My coach advised me to bowl with leather ball and I just followed him. That helped me gain confidence,” he said
“After that, I have worked on my bowling and mastered left-arm swing. That has helped me immensely and later, when I travelled to the National Cricket Academy, I learned how to develop overall skills. I am reaping those benefits,” Singh added.
Read Comments


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{include file='public@base:head' /}
{include file='public@base:top_menu'} {include file='public@base:side_menu'} {block name="main-content"}{/block}
{block name="javascript"}{/block}


--- layout: page title: Unni categories: [] tags: [] status: publish type: page published: true meta: _wp_page_template: default author: ---
Final year Computer Science student at Govt. Engineering College, Trichur.
Proud Indian
Python Freak. Luxurious Perl,C, C++ coder
Cartoonist,Artist,Designer.
Love working on GUIs
Son.
Brother.
Lover.
Friend.
Get me here<EMAIL_ADDRESS>/ kpnunni@chipmonks.co.in


WITNESS LIST Higher Education Committee February 5, 1998-9:00A Hopwood/H.B. 588 For: Coronado, Jim (Judge) (self, Judge) Martinez, Roxane E. (self, Judge, MABA of TX) On: Alpern, Robert J., M.D. (UT Southwestern Medical) Ballard, Billy R., M.D. (UTMB @ Galveston) Bowen, Ray M. (self, Pres. TAMU) Buchanan, Richard N. (Baylor College Dentist.) Castro, Gilbert A. (self, UTHSC-Houston) Cunningham, William H. (UT System) Davis, Elizabeth (Univ. North TX HSC) Don Brown (TX Higher Ed. Coord.Brd) Friedland, Michael L. (TAMUHSC College of Med.) Gunn, Albert E., M.D. (UTHSC-Houston) Juarez, Jose Roberto Jr (St. Mary's Law School) Kupersmith, Joel (TX Tech School of Med.) Maldonado, Filomeno G. (TAMUHSC Collge of Med.) Michael, Lloyd H. (Baylor College of Med.) Mullins, Charles B., MD (UT System) Newton, Frank W. (self, TX Tech Law) Read, Frank T. (STX College of Law-A&M) Rogers, Paul C. (SMU School of Law) Seymore, Malinda L. (self, TX Wesleyan Law) Sharlot, Michael M. (UT Law School) Smith, Arthur K. (self, U of H) Smith, David R., M.D. (TX Tech Univ. HSC) Thompson, Barry B. (TX A&M Univ. System) Thomson, William A. (Baylor College of Med.) Toben, Bradley, J.B. (Baylor School of Law) Torres, Gerald (UT Law School) Weeden, Darnell L. (Thurgood Marshall Law) Williams, Diane, L. (Univ. North TX HSC) Young, James J. Ph.D. (UTHSC-San Antonio Med.) Zamora, Stephen (self, U of H Law Center)


Slides http://bit.ly/fra-apps
autonomous learning does not exist in machines
AI or ML are not like hyper intelligent humans
who never sleep and
do not want more money
if age < 25:
if speed > 140:
return red # young people, fast cars: high risk
else:
return yellow # young people: medium risk
if age > 75:
return red # old people: high risk
if miles_per_year > 30:
return red # a lot of driving: high risk
if miles_per_year > 20:
return yellow # a bit of driving: medium risk
return green # otherwise: low risk
Looking at the problem from the perspective of our data
Oliver Zeigermann / @DJCordhose
Chief Visionary Officer, embarc Software Consulting GmbH


Challenges |
Verification of Electrical circuit:-It is not good practice to directly design PCB for the proposed electrical circuit of the project. Therefore it is first done on the breadboard with physical connecting each and every components through jumper wires. Obviously it is very hectic and tiring approach but as well very much necessary. This is the first step toward project hardware so is time taking. Below snap is of our work while checking our proposed electrical circuit. | /tr>
|||||||||
PCB Layout:-One of the big issue with our project was “How to set PCB layout for the 3x3 led matrix display. The main problem was that the leds work in a cascaded manner so they are needed to be connected in a very precise and carefull manner. After lots of consideration we decided to design the PCB layout in such a way that the leds will be placed serially on the PCB board 3 leds on a row and they will be connected such that the data-out connection from each led will be connected to data-in connection. In this way the PCB Layout is set for our project. One of the major issue which we faced that the output were inverted as shown in the below diagram. We were surprised with such results because everything was working well but the output was inverted every time the code was run. This was a real concern for us because we could not move on until we solve this problem. In the previous led matrix the leds were configured from 1-8 as the columns. With the new matrix the configuration had totally changed now the leds were configured in a vice versa (i.e) from 8-0 as the columns for the leds. After connecting led matrix in the right way we got this issue solved. This was done because of the combined efforts of our team members. PCB Routing:-One of the most hectic and complicated job in our project was routing that board file in such a way that there must be no crossover. Another challenge was that it must be one layer PCB which increases over burden. We tried our best to reduce and avoid as much cross over as possible. Below is the image of unrouted PCB board which almost took us two days to route.First when we routed our board file all the components were not mirrored so we have to solder it on upper (cupper side) of the sheet which was very difficult to solder. After that we mirror every component and route board file again.After much efforts we were able to route our pcb. Program CodeTo show any specific pattern on the led matrix Display requires thorough understanding and deep concept of the functions used in the coding. To set the code for desired output like in our case an analog clock pattern was a tricky and challenging task. We had to search out many different websites and took the help of our coordinators. We also had to gone through different libraries and their functions to understand how the codes are working. This took many weeks with day by day progress towards our objective. At last after lot of efforts, We got it, we successfully set the code for our desired output.The credit for this goes to our team members, Lab Assistances and Supervisor, who helped around-the-clock to make this happen. |


Constructor
new BITable(code, name, columns, parentopt, childrenopt)
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
code
|
string |
Code for the presentation table, usually the same as the name. |
|
name
|
string |
Name for the presentation table, usually the same as the code. |
|
columns
|
Array.<Array.BIColumn> |
Array of |
|
parent
|
string |
<optional> |
Code of the parent table if it exists. |
children
|
Array.<Array.BITable> |
<optional> |
Array of |
Members
Children
Array of BITable
objects indicating the child tables belonging to this table.
Code
Code for the presentation table, usually the same as the name.
Columns
columns Array of BIColumn
objects belonging to this table.
Description
Description for the presentation table.
Name
Name for the presentation table, usually the same as the code.
Parent
Code of the parent table if it exists.
Type
Hardcoded type identifier for the object: Table
.


MUSIC
Moon Pond Flow
Game – Chiptune – Swing
Here's a fun, swingy chiptune piece. This is my newest released song as of February 2016.
Ravagen Theme
Game – Space – Minimalistic
This is the newest rendition of the theme to my game Ravagen. I think the minimalism of it helps emphasize the emptiness of space, which is really fitting for Ravagen's story.
Reflections (WIP)
Vocals – Electronic
Reflections is a collab between a friend and I which we intend to return to and finish one day. Our main focus for this song was the lyrics, and figuring out how we worked as a team. I'd say it went well!
Wary Triumph
Game – Retro – Loop
I made this tune during a game jam with a friend. It served as our overworld theme, although I think it would be more fitting for a subtly mysterious area.
Uncertainty
Vocals – Dance – EDM
Here's a fun, little ditty I put together after returning from Japan. I tried mixing some older-sounding influences with newer ones and ended up with a catchy little tune.
Stargaze (WIP)
Electronic – Psychedelic Rock
This is a newer song that I've been working on, but haven't quite finished yet. Tame Impala definitely served as some inspiration for this song.
I Fell From The Sky Too
Game – Chill – Loop
Here's a calming tune I wrote with the intention of being the BGM for an overworld or title screen.


From pull to push
Opening new possibilities
Trygve Lie / Erlend Nossum
Classic web model
Ajax web model
Comet web model
WebSockets
- Full duplex over a single TCP socket
- Exposed via a JavaScript interface
- Native support in browsers
- Can extend any protocol
- Can handle binary
- Is a standard
WebSockets
WebSockets
Possibilities
Push do open
for new possibilities
never seen in the browser!
SemiSpace is an example
What is?
SemiSpace is a light weight
Open Source interpretation of
Tuple Space / Object Space
Suited for
- propagation of short lived states
- distribution of objects
- distributed master / worker (or actor) pattern
- time boxed queries
- proxying data from within internal net
Working with
SemiLease write (Object obj, long lifeTimeInMs );
Object read (Object template, long timeout);
Object readIfExists (Object template);
Object take (Object template, long timeout);
Object takeIfExists (Object template);
SemiEventRegistration notify
(Object template,
SemiEventListener listener,
long duration);
Working with
Working with
Working with
Working with
Working with
Working with
Working with
Working with
Distributed JSON
Object:
{Video : {
user : "John",
fileName : "tour.avi",
converted : false
}}
Template:
{Video : {
user : "John"
converted : true
}}
Support
- Comet
- Distribution of any JSON object between browsers out of the box
- The benefits of a space brought to the browser
- JSON to Java Object exchange
- A simple JavaScript API
Connection object
var comet = new semispace.Comet(cometImpl, server);
comet.addMetaListener(function(status, message){
// server messages and server status
});
SemiSpace object
var space = new semispace.SemiSpace(connectionObject);
space.notify(template, listener, duration, callback);
space.write(obj, lifeTimeInMs, callback);
space.read(template, timeout, callback);
space.readIfExists(template, callback);
space.take(template, timeout, callback);
space.takeIfExists(template, callback);
Future directions
- A security module:
- Implement the cometd security model
- Add a JSONschema-based configurable object filter
- Binding for JQuery
- Free the library of the necessity of bindings (DOJO / JQuery)
- Upgrade to cometd-2.0
- WebSocket support
- Add handy tools (compression tools, proxy, DB binding etc..)


Take a look at our Video!
This video shows the evolution in number of accidents across the UK throughout the years.
In the bargraphs displayed, the orange bar corresponds to the number of 'Serious' accidents, and the blue bar corresponds to the number of 'Fatal' accidents that have occurred in that district.
The heatmap underneath the bargraphs represents the number of hospitals in that area.
Let's analyse what we're seeing with two simple questions:
1. Is there any correlation between areas with the most number of accidents and the most hospitals around?
2. How is the number of accidents changing with time?
Visit our analysis section for more insight into the causes of why this is happening!