Hi, what do you mean "openejb-client-3.1.4 " ? and how ?
Thanks.
Edward
On 5/11/2011 1:03 AM, Romain Manni-Bucau wrote:
> hi,
>
> can you have a try doing a client with openejb-client-3.1.4?
>
> - Romain
>
> 2011/5/11<EMAIL_ADDRESS>>
>> Hi, Romain,
>> I am not using Tomcat, just openEJB 3.1.4, ejb jar is copied into app
>> folder. I tried : properties.put(Context.PROVIDER_URL,
>> "ejbd://localhost:4201"), still doesn't work.
>>
>> //////////////
>> C:\openejb-3.1.4\bin>openejb start
>> Apache OpenEJB 3.1.4 build: 20101112-03:32
>> http://openejb.apache.org/
>> log4j:WARN No appenders could be found for logger
>> (org.apache.openejb.resource.activemq.ActiveMQResourceAdapter).
>> log4j:WARN Please initialize the log4j system properly.
>> [init] OpenEJB Remote Server
>> ** Starting Services **
>> NAME IP PORT
>> httpejbd <IP_ADDRESS> 4204
>> admin thread <IP_ADDRESS> 4200
>> ejbd <IP_ADDRESS> 4201
>> ejbd <IP_ADDRESS> 4203
>> hsql <IP_ADDRESS> 9001
>> telnet <IP_ADDRESS> 4202
>> -------
>> Ready!
>> ///////////////////
>>
>> error message is
>> ///////////
>> Exception in thread "main" javax.naming.NamingException: Cannot lookup
>> '/CalculatorImplRemote'. [Root exception is java.rmi.RemoteException: Cannot
>> deternmine server protocol version: Received null/0.0; nested exception is:
>> java.io.IOException: Unable to read protocol version. Reached the end
>> of the stream.]
>>
>> at org.apache.openejb.client.JNDIContext.lookup(JNDIContext.java:240)
>> at javax.naming.InitialContext.lookup(Unknown Source)
>> at com.Test.main(Test.java:20)
>> Caused by: java.rmi.RemoteException: Cannot deternmine server protocol
>> version: Received null/0.0; nested exception is:
>> java.io.IOException: Unable to read protocol version. Reached the end
>> of the stream.
>> at org.apache.openejb.client.Client.processRequest(Client.java:151)
>>
>> at org.apache.openejb.client.Client.request(Client.java:42)
>> at org.apache.openejb.client.JNDIContext.request(JNDIContext.java:87)
>> at org.apache.openejb.client.JNDIContext.lookup(JNDIContext.java:234)
>> ... 2 more
>> Caused by: java.io.IOException: Unable to read protocol version. Reached
>> the end of the stream.
>> at
>> org.apache.openejb.client.ProtocolMetaData.readExternal(ProtocolMetaData.java:89)
>> at org.apache.openejb.client.Client.processRequest(Client.java:148)
>> ... 5 more
>>
>> ///////////////////
>>
>> Thanks.
>> Edward
>>
>>
>>
>>
>> On 5/10/2011 1:20 AM, Romain Manni-Bucau wrote:
>>
>>> Hi Edward,
>>>
>>> i guess yo are trying to lookup an object in tomcat (because of the http
>>> url), if tomcat is on the 4204 port the url should be
>>> http://localhost:4204/<openejb
>>> context name>/ejb, it is often http://localhost/openejb/ejb.
>>>
>>> If you are not using tomcat can you please explain us how you run your
>>> server.
>>>
>>> - Romain
>>>
>>> 2011/5/10<EMAIL_ADDRESS>>>>
>>> Hi, ALL,
>>>> I am trying to write a standalone EJB client, but failed.
>>>>
>>>> I have a standalone OpenEJB in C drive, I place simple-stateless-1.0.jar
>>>> into its app folder. This jar is from OpenEjb example. It works, from log
>>>> I
>>>> can see,
>>>>
>>>> ///////////////
>>>> 2011-05-09 21:07:30,825 - INFO - Jndi(name=CalculatorImplLocal) -->
>>>> Ejb(deployment-id=CalculatorImpl)
>>>> 2011-05-09 21:07:30,827 - INFO - Jndi(name=CalculatorImplRemote) -->
>>>> Ejb(deployment-id=CalculatorImpl)
>>>> 2011-05-09 21:07:30,856 - INFO - Created
>>>> Ejb(deployment-id=CalculatorImpl,
>>>> ejb-name=CalculatorImpl, container=My Stateless Container)
>>>> 2011-05-09 21:07:30,856 - INFO - Deployed
>>>> Application(path=C:\openejb-3.1.4\apps\simple-stateless-1.0.jar)
>>>> ///////////////
>>>>
>>>> Now the clinent is
>>>> //////////////////////
>>>> Properties properties = new Properties();
>>>> properties.setProperty(Context.INITIAL_CONTEXT_FACTORY,
>>>> "org.apache.openejb.client.RemoteInitialContextFactory");
>>>> //properties.put("java.naming.provider.url",
>>>> "ejbd://localhost:4201");
>>>> properties.put("java.naming.provider.url", "
>>>> http://localhost:4204/ejb");
>>>>
>>>> InitialContext initialContext = new InitialContext(properties);
>>>>
>>>> Object object = initialContext.lookup("CalculatorImplRemote");
>>>> /////////////////////////////
>>>> It always give me error:
>>>>
>>>> /////////////////
>>>> Exception in thread "main" javax.naming.NamingException: Cannot lookup
>>>> '/CalculatorImplRemote'. [Root exception is java.rmi.RemoteException:
>>>> Cannot
>>>> open object input stream to server (OEJP/3.1) : null; nested exception
>>>> is:
>>>> java.io.EOFException]
>>>> at org.apache.openejb.client.JNDIContext.lookup(JNDIContext.java:240)
>>>> at javax.naming.InitialContext.lookup(Unknown Source)
>>>> at com.Test.main(Test.java:20)
>>>> Caused by: java.rmi.RemoteException: Cannot open object input stream to
>>>> server (OEJP/3.1) : null; nested exception is:
>>>> java.io.EOFException
>>>> at org.apache.openejb.client.Client.processRequest(Client.java:159)
>>>> at org.apache.openejb.client.Client.request(Client.java:42)
>>>> at org.apache.openejb.client.JNDIContext.request(JNDIContext.java:87)
>>>> at org.apache.openejb.client.JNDIContext.lookup(JNDIContext.java:234)
>>>> ... 2 more
>>>> Caused by: java.io.EOFException
>>>> at java.io.ObjectInputStream$PeekInputStream.readFully(Unknown Source)
>>>> at java.io.ObjectInputStream$BlockDataInputStream.readShort(Unknown
>>>> Source)
>>>> at java.io.ObjectInputStream.readStreamHeader(Unknown Source)
>>>> at java.io.ObjectInputStream.<init>(Unknown Source)
>>>> at
>>>>
>>>> org.apache.openejb.client.EjbObjectInputStream.<init>(EjbObjectInputStream.java:31)
>>>> at org.apache.openejb.client.Client.processRequest(Client.java:156)
>>>> ... 5 more
>>>>
>>>> ////////
>>>> I am using openejb-itests-standalone-client-3.0.jar as its reference
>>>> api.
>>>>
>>>> Please help me on this.
>>>>
>>>> Thanks
>>>> Edward
>>>>
>>>>
>>>>
>>>>
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#include <routing.h>
Definition at line 1789 of file routing.h.
◆ PickupDeliveryStatus
| Enumerator |
|---|
| PICKUP | |
| DELIVERY | |
| NONE | |
Definition at line 1798 of file routing.h.
◆ TypeRegulationsChecker()
| operations_research::TypeRegulationsChecker::TypeRegulationsChecker |
( |
const RoutingModel & |
model | ) |
|
|
explicit |
◆ ~TypeRegulationsChecker()
| virtual operations_research::TypeRegulationsChecker::~TypeRegulationsChecker |
( |
| ) |
|
|
inlinevirtual |
◆ CheckVehicle()
| bool operations_research::TypeRegulationsChecker::CheckVehicle |
( |
int |
vehicle, |
|
|
const std::function< int64(int64)> & |
next_accessor |
|
) |
| |
◆ GetNonDeliveryCount()
| int operations_research::TypeRegulationsChecker::GetNonDeliveryCount |
( |
int |
type | ) |
const |
|
protected |
◆ GetNonDeliveredCount()
| int operations_research::TypeRegulationsChecker::GetNonDeliveredCount |
( |
int |
type | ) |
const |
|
protected |
◆ HasRegulationsToCheck()
| virtual bool operations_research::TypeRegulationsChecker::HasRegulationsToCheck |
( |
| ) |
const |
|
protectedpure virtual |
◆ CheckTypeRegulations()
| virtual bool operations_research::TypeRegulationsChecker::CheckTypeRegulations |
( |
int |
type | ) |
const |
|
protectedpure virtual |
◆ model_
| const RoutingModel& operations_research::TypeRegulationsChecker::model_ |
|
protected |
The documentation for this class was generated from the following file:


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"""Matrix factorization with Sparse PCA"""
# Author: Vlad Niculae, Gael Varoquaux, Alexandre Gramfort
# License: BSD 3 clause
import warnings
import numpy as np
from ..utils import check_random_state, check_array
from ..utils.validation import check_is_fitted
from ..linear_model import ridge_regression
from ..base import BaseEstimator, TransformerMixin
from .dict_learning import dict_learning, dict_learning_online
class SparsePCA(BaseEstimator, TransformerMixin):
"""Sparse Principal Components Analysis (SparsePCA)
Finds the set of sparse components that can optimally reconstruct
the data. The amount of sparseness is controllable by the coefficient
of the L1 penalty, given by the parameter alpha.
Read more in the :ref:`User Guide <SparsePCA>`.
Parameters
----------
n_components : int,
Number of sparse atoms to extract.
alpha : float,
Sparsity controlling parameter. Higher values lead to sparser
components.
ridge_alpha : float,
Amount of ridge shrinkage to apply in order to improve
conditioning when calling the transform method.
max_iter : int,
Maximum number of iterations to perform.
tol : float,
Tolerance for the stopping condition.
method : {'lars', 'cd'}
lars: uses the least angle regression method to solve the lasso problem
(linear_model.lars_path)
cd: uses the coordinate descent method to compute the
Lasso solution (linear_model.Lasso). Lars will be faster if
the estimated components are sparse.
n_jobs : int,
Number of parallel jobs to run.
U_init : array of shape (n_samples, n_components),
Initial values for the loadings for warm restart scenarios.
V_init : array of shape (n_components, n_features),
Initial values for the components for warm restart scenarios.
verbose : int
Controls the verbosity; the higher, the more messages. Defaults to 0.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Attributes
----------
components_ : array, [n_components, n_features]
Sparse components extracted from the data.
error_ : array
Vector of errors at each iteration.
n_iter_ : int
Number of iterations run.
See also
--------
PCA
MiniBatchSparsePCA
DictionaryLearning
"""
def __init__(self, n_components=None, alpha=1, ridge_alpha=0.01,
max_iter=1000, tol=1e-8, method='lars', n_jobs=1, U_init=None,
V_init=None, verbose=False, random_state=None):
self.n_components = n_components
self.alpha = alpha
self.ridge_alpha = ridge_alpha
self.max_iter = max_iter
self.tol = tol
self.method = method
self.n_jobs = n_jobs
self.U_init = U_init
self.V_init = V_init
self.verbose = verbose
self.random_state = random_state
[docs] def fit(self, X, y=None):
"""Fit the model from data in X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training vector, where n_samples in the number of samples
and n_features is the number of features.
Returns
-------
self : object
Returns the instance itself.
"""
random_state = check_random_state(self.random_state)
X = check_array(X)
if self.n_components is None:
n_components = X.shape[1]
else:
n_components = self.n_components
code_init = self.V_init.T if self.V_init is not None else None
dict_init = self.U_init.T if self.U_init is not None else None
Vt, _, E, self.n_iter_ = dict_learning(X.T, n_components, self.alpha,
tol=self.tol,
max_iter=self.max_iter,
method=self.method,
n_jobs=self.n_jobs,
verbose=self.verbose,
random_state=random_state,
code_init=code_init,
dict_init=dict_init,
return_n_iter=True
)
self.components_ = Vt.T
self.error_ = E
return self
class MiniBatchSparsePCA(SparsePCA):
"""Mini-batch Sparse Principal Components Analysis
Finds the set of sparse components that can optimally reconstruct
the data. The amount of sparseness is controllable by the coefficient
of the L1 penalty, given by the parameter alpha.
Read more in the :ref:`User Guide <SparsePCA>`.
Parameters
----------
n_components : int,
number of sparse atoms to extract
alpha : int,
Sparsity controlling parameter. Higher values lead to sparser
components.
ridge_alpha : float,
Amount of ridge shrinkage to apply in order to improve
conditioning when calling the transform method.
n_iter : int,
number of iterations to perform for each mini batch
callback : callable or None, optional (default: None)
callable that gets invoked every five iterations
batch_size : int,
the number of features to take in each mini batch
verbose : int
Controls the verbosity; the higher, the more messages. Defaults to 0.
shuffle : boolean,
whether to shuffle the data before splitting it in batches
n_jobs : int,
number of parallel jobs to run, or -1 to autodetect.
method : {'lars', 'cd'}
lars: uses the least angle regression method to solve the lasso problem
(linear_model.lars_path)
cd: uses the coordinate descent method to compute the
Lasso solution (linear_model.Lasso). Lars will be faster if
the estimated components are sparse.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Attributes
----------
components_ : array, [n_components, n_features]
Sparse components extracted from the data.
error_ : array
Vector of errors at each iteration.
n_iter_ : int
Number of iterations run.
See also
--------
PCA
SparsePCA
DictionaryLearning
"""
def __init__(self, n_components=None, alpha=1, ridge_alpha=0.01,
n_iter=100, callback=None, batch_size=3, verbose=False,
shuffle=True, n_jobs=1, method='lars', random_state=None):
super(MiniBatchSparsePCA, self).__init__(
n_components=n_components, alpha=alpha, verbose=verbose,
ridge_alpha=ridge_alpha, n_jobs=n_jobs, method=method,
random_state=random_state)
self.n_iter = n_iter
self.callback = callback
self.batch_size = batch_size
self.shuffle = shuffle
[docs] def fit(self, X, y=None):
"""Fit the model from data in X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training vector, where n_samples in the number of samples
and n_features is the number of features.
Returns
-------
self : object
Returns the instance itself.
"""
random_state = check_random_state(self.random_state)
X = check_array(X)
if self.n_components is None:
n_components = X.shape[1]
else:
n_components = self.n_components
Vt, _, self.n_iter_ = dict_learning_online(
X.T, n_components, alpha=self.alpha,
n_iter=self.n_iter, return_code=True,
dict_init=None, verbose=self.verbose,
callback=self.callback,
batch_size=self.batch_size,
shuffle=self.shuffle,
n_jobs=self.n_jobs, method=self.method,
random_state=random_state,
return_n_iter=True)
self.components_ = Vt.T
return self