We Keep Coding

how to make the latest MyBatis Dynamic SQL support pagination for mysql (limit/offset)?

There are some samples on http://www.mybatis.org/mybatis-dynamic-sql/docs/select.html.
I want to implement limit/offset for mysql but failed to see any document on describing how to extend this library to support additional where condition.
here is what i'd like to achieve:
SelectStatementProvider selectStatement = select(id, animalName, bodyWeight, brainWeight)
.from(animalData)
.where(id, isIn(1, 5, 7))
.and(bodyWeight, isBetween(1.0).and(3.0))
.orderBy(id.descending(), bodyWeight)
.limit(1).offset(10)
.build()
.render(RenderingStrategy.MYBATIS3);

There are a couple of resources you can use.
This page - http://www.mybatis.org/mybatis-dynamic-sql/docs/whereClauses.html - shows an example of using standalone where clauses to build a paging query. This is not exactly what you are looking for, but it shows one way to do it.
There is a unit test showing something that is closer to what you are looking for here - https://github.com/mybatis/mybatis-dynamic-sql/tree/master/src/test/java/examples/paging. This code works for MySQL and you could use it as is.
I hope to make this a little easier in a future release.

How to correctly update SNMP4j agent MIB values correctly

I'm trying to create an SNMP4j agent and am finding it difficult to understand the process correctly. I have successfully created an agent that can be queried from the command line using snmpwalk. What I am having difficulty with is understanding how I am meant to update the values stored in my implemented MIB.
The following shows the relevant code I use for creating the MIB (I implement Host-Resources-MIB)
agent = new Agent("0.0.0.0/" + port);
agent.start();
agent.unregisterManagedObject(agent.getSnmpv2MIB());
modules = new Modules(DefaultMOFactory.getInstance());
HrSWRunEntryRow thisRow = modules.getHostResourcesMib().getHrSWRunEntry()
.createRow(oidHrSWRunEntry);
final OID ashEnterpriseMIB = new OID(".1.3.6.1.4.1.49266.0");
thisRow.setHrSWRunIndex(new Integer32(1));
thisRow.setHrSWRunName(new OctetString("RunnableAgent"));
thisRow.setHrSWRunID(ashEnterpriseMIB);
thisRow.setHrSWRunPath(new OctetString("All is good in the world")); // Max 128 characters
thisRow.setHrSWRunParameters(new OctetString("Everything is working")); // Max 128 characters
thisRow.setHrSWRunType(new Integer32(HrSWRunTypeEnum.application));
thisRow.setHrSWRunStatus(new Integer32(HrSWRunStatusEnum.running));
modules.getHostResourcesMib().getHrSWRunEntry().addRow(thisRow);
agent.registerManagedObject(modules.getHostResourcesMib());
This appears to be sufficient to create a runnable agent. What I do not understand is how I am meant to change the values stored in the MIB (how do I, for example, change the value of HrSWRunStatus). There seem to be a few kludge ways but they don't seem to fit with the way the library is written.
I have come across numerous references to using/overriding the methods
prepare
commit
undo
cleanup
But cannot find any examples where this is done. Any help would be gratefully received.

In protected void registerManagedObjects(), you need to do something like new MOMutableColumn(columnId, SMIConstants.SYNTAX_INTEGER, MOAccessImpl.ACCESS_READ_WRITE, null); for your HrSWRunStatus. Take a look at the TestAgent.java example of SNMP4J-agent source.

How to create a tensorflow serving client for the 'wide and deep' model?

I've created a model based on the 'wide and deep' example (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/learn/wide_n_deep_tutorial.py).
I've exported the model as follows:
m = build_estimator(model_dir)
m.fit(input_fn=lambda: input_fn(df_train, True), steps=FLAGS.train_steps)
results = m.evaluate(input_fn=lambda: input_fn(df_test, True), steps=1)
print('Model statistics:')
for key in sorted(results):
print("%s: %s" % (key, results[key]))
print('Done training!!!')
# Export model
export_path = sys.argv[-1]
print('Exporting trained model to %s' % export_path)
m.export(
export_path,
input_fn=serving_input_fn,
use_deprecated_input_fn=False,
input_feature_key=INPUT_FEATURE_KEY
My question is, how do I create a client to make predictions from this exported model? Also, have I exported the model correctly?
Ultimately I need to be able do this in Java too. I suspect I can do this by creating Java classes from proto files using gRPC.
Documentation is very sketchy, hence why I am asking on here.
Many thanks!

I wrote a simple tutorial Exporting and Serving a TensorFlow Wide & Deep Model.
TL;DR
To export an estimator there are four steps:
Define features for export as a list of all features used during estimator initialization.
Create a feature config using create_feature_spec_for_parsing.
Build a serving_input_fn suitable for use in serving using input_fn_utils.build_parsing_serving_input_fn.
Export the model using export_savedmodel().
To run a client script properly you need to do three following steps:
Create and place your script somewhere in the /serving/ folder, e.g. /serving/tensorflow_serving/example/
Create or modify corresponding BUILD file by adding a py_binary.
Build and run a model server, e.g. tensorflow_model_server.
Create, build and run a client that sends a tf.Example to our tensorflow_model_server for the inference.
For more details look at the tutorial itself.

Just spent a solid week figuring this out. First off, m.export is going to deprecated in a couple weeks, so instead of that block, use: m.export_savedmodel(export_path, input_fn=serving_input_fn).
Which means you then have to define serving_input_fn(), which of course is supposed to have a different signature than the input_fn() defined in the wide and deep tutorial. Namely, moving forward, I guess it's recommended that input_fn()-type things are supposed to return an InputFnOps object, defined here.
Here's how I figured out how to make that work:
from tensorflow.contrib.learn.python.learn.utils import input_fn_utils
from tensorflow.python.ops import array_ops
from tensorflow.python.framework import dtypes
def serving_input_fn():
features, labels = input_fn()
features["examples"] = tf.placeholder(tf.string)
serialized_tf_example = array_ops.placeholder(dtype=dtypes.string,
shape=[None],
name='input_example_tensor')
inputs = {'examples': serialized_tf_example}
labels = None # these are not known in serving!
return input_fn_utils.InputFnOps(features, labels, inputs)
This is probably not 100% idiomatic, but I'm pretty sure it works. For now.

JPivot Display Mondrian Result

I am trying to display the result of a Mondrian query using JPivot. Many examples are showing how to use the tag library for JSP but I need to use the Java API, I looked at the documentation but I cannot understand how to use it to display the results in the table. Here is my code
Query query = connection.parseQuery(mdxQuery);
Result result = connection.execute(query);
result.print(new PrintWriter(System.out,true));
I would like to know if I can use the result object to build the jpivot table.
Thanks in advance!

First of all, using JPivot
is a pretty bad idea.
It was discontinued back in 2008.
There is a good project which is intended to replace the JPivot called Pivot4j. Despite it is currently under development (0.8 -> 0.9 version), Pivot4j can actually do the business.
However, if we're talking about your case:
result.print(new PrintWriter(System.out,true));
This string prints the HTML code with OLAP cube into your System.out.
You can write the HTML code in some output stream (like FileOuputStream), and then display it.
OutputStream out = new FileOutputStream("result.html");
result.print(new PrintWriter(out, true));
//then display this file in a browser
However, if you want to have the same interface as in JPivot, I don't think there is an easy way to do it without .jsp. In these case I strongly recommend you to try Pivot4j.
Good luck!

Embedded Tweets in Vaadin

I am trying to embed a tweet in Vaadin v7:
Label oneTweet = new Label();
String s = "<blockquote class=\"twitter-tweet\"><p>Four more years. twitter.com/BarackObama/st…</p>— Barack Obama (#BarackObama) November 7, 2012</blockquote>";
s = s + "<script async src=\"http://platform.twitter.com/widgets.js\" charset=\"utf-8\"></script>";
oneTweet.setValue(s);
oneTweet.setContentMode(ContentMode.HTML);
layout.addComponent(oneTweet);
The problem is, Vaadin does not pick up the script file widget.js. I tried forcing it by putting
#JavaScript( {"http://platform.twitter.com/widgets.js"} )
at the beginning of my source code. It picks up the file but does not style the embedded tweet at all. I was wondering if someone has done this before.

Does it work with the ContentMode Label.CONTENT_XHTML or Label.CONTENT_RAW? Another guess that comes to mind is to include the script in your gwt.xml file and take it out from the Label. Good luck.

In case someone else is having the same question, I solved it by calling this at the end of loading a tweet:
String script = "!function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0];if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=\"http://platform.twitter.com/widgets.js\";fjs.parentNode.insertBefore(js,fjs);}}(document,\"script\",\"twitter-wjs\");";
Page.getCurrent().getJavaScript().execute(script);
The javascript snippet comes from the guideline by Twitter (Troubleshooting).