So is there any way to generate simple "Pojo" Python classes from Java code?

I had a go at it and below is the solution:

Considering below simplistic Pojo.java

public class Pojo {
    private String string = "default";
    public int integer = 1;
    public String getString(){
        return string;
    }
}

The solution will need 3 phases

1. Java Pojo to JSON Schema

I could find below options:

1. FasterXML/jackson-module-jsonSchema: This is the base, which below libraries also use internally.
2. mbknor/mbknor-jackson-jsonSchema: Officially cited by above to support the v4 of the json schema.
3. reinert/JJSchema

With below relevant code with option 1(also go through the site):

ObjectMapper MAPPER = new ObjectMapper();
JsonSchemaGenerator generator = new JsonSchemaGenerator(MAPPER);
JsonSchema jsonSchema = generator.generateSchema(Pojo.class);
System.out.println(MAPPER.writeValueAsString(jsonSchema));

Below output json schema string is got:

{"type":"object","id":"urn:jsonschema:Pojo","properties":{"string":{"type":"string"},"integer":{"type":"integer"}}}

2. JSON Schema post-process

This phase is required mainly because I found that for the simplistic use case(at least), Step 3 below needs a json schema that has a definitions property mandatorily. I guess this is because of the evolving schema definitions @ http://json-schema.org/. Also, we can include a title property to specify the name of the python class that next step will generate.

We can easily accomplish these in the java program of Step 1 above as a post step. We need a json schema string of below form:

{"definitions": {}, "title": "Pojo", "type":"object","id":"urn:jsonschema:Pojo","properties":{"string":{"type":"string"},"integer":{"type":"integer"}}}

Notice that only addition is "definitions": {}, "title": "Pojo"

3. Json schema to Python class

frx08/jsonschema2popo seems to be doing this job quite nicely.

pip install jsonschema2popo
jsonschema2popo -o /path/to/output_file.py /path/to/json_schema.json

Some more points

1. The Java-Json schema generators will only include those properties in the output which are either public or have a public getter.
2. I assume that for a mass migration annotating the Java classes will be a pain. Otherwise, if this is feasible to you, all the above java libraries provide rich annotations where you can specify whether a property is mandatory and much more.

You could also use Py4J. There is an example on the frontpage and lots of documentation, but essentially, you just call Java methods from your python code as if they were python methods:

from py4j.java_gateway import JavaGateway
gateway = JavaGateway()                        # connect to the JVM
java_object = gateway.jvm.mypackage.MyClass()  # invoke constructor
other_object = java_object.doThat()
other_object.doThis(1,'abc')
gateway.jvm.java.lang.System.out.println('Hello World!') # call a static method

As opposed to Jython, one part of Py4J runs in the Python VM so it is always "up to date" with the latest version of Python and you can use libraries that do not run well on Jython (e.g., lxml). The other part runs in the Java VM you want to call.

The communication is done through sockets instead of JNI and Py4J has its own protocol (to optimize certain cases, to manage memory, etc.)

Disclaimer: I am the author of Py4J

In short, you can't run Java code natively in a CPython interpreter.

Firstly, Python is just the name of the specification for the language. If you are using the Python supplied by your operating system (or downloaded from the official Python website), then you are using CPython. CPython does not have the ability to interpret Java code.

However, as you mentioned, there is an implementation of Python for the JVM called Jython. Jython is an implementation of Python that operates on the JVM and therefore can interact with Java modules. However, very few people work with Jython and therefore you will be a bit on your own about making everything work properly. You would not need to re-write your vanilla Python code (since Jython can interpret Python 2.x) but not all libraries (such as numpy) will be supported.

Finally, I think you need to better understand the K-Means algorithm, as the algorithm is implicitly defined in terms of the Euclidean distance. Using any other distance metric would no longer be considered K-Means and may affect the convergence of the algorithm. See here for more information.

Again, you can't run Java code natively in a CPython interpreter. Of course there are various third party libraries that will handle marshalling of data between Java and Python. However, I stand by my statement that for this particular use case you are likely better to use a native Python library (something like K-Medoid in Scikit-Learn). Attempting to call through to Java, with all the associated overhead, is overkill for this problem, in my opinion.