semvec/pypi-packages huggingface dataset:
check50-java 0.5
A check50 extension for java
Requires Python: >=3.6

# check50_java

This is an extension for the [CS50 automarker check50][check50] that provides convenient wrappers around [`check50.run`][run] for directly compiling and interpreting Java source/byte code.

See also [check50_junit](https://github.com/pazz/check50_junit) and [check50_checkstyle](https://github.com/pazz/check50_checkstyle) for related utilities that make writing check50 checks for java less painful.


## Example Usage

All examples below assume that you're importing `check50` and `check50_java`.

### Compile Java source code

```python
@check50.check()
def someclass_compiles():
    check50_java.compile("SomeClass.java"
        classpaths=['your/classpaths',
                    'relative/to/the',
                    'pset/directory.jar']
    )
```
The classpaths argument defaults to `None` ~ `'.'`.

### Check that a class is executable (has well-formed main method)

```python
@check50.check(someclass_compiles)
def someclass_main_exists():
    """SomeClass is application class"""
    check50_java.checks.is_application_class("SomeClass")
```

### Execute an application class and check that its output is as expected

```python
@check50.check(someclass_main_exists)
def someclass_main_output():
    """SomeClass.main() output"""
    expected = "X"
    actual = check50_java.run("SomeClass").stdout()
    help_msg = "did you introduce training newline or whitespace characters?"
    if actual != expected:
        raise check50.Mismatch(expected, actual, help=help_msg)
```


[check50]: https://github.com/cs50/check50
[run]: https://cs50.readthedocs.io/projects/check50/en/latest/api/#check50.run
ad-interface-functions 0.21
Interface functions used in the autonomous driving stack at TUM/FTM

# Description
This repository provides some interface functions we frequently use in our trajectory planning software stack at FTM/TUM.
Next to the simple UDP interface we also use ZeroMQ (http://zeromq.org/) and its Python bindings PyZMQ.

# List of components
* `udp_export`: UDP export function.
* `udp_import`: UDP import function.
* `zmq_export`: ZMQ export function handling pyobj, json, str.
* `zmq_import`: ZMQ import function handling pyobj, json, str.
* `zmq_import_poll`: ZMQ import function handling pyobj with polling option.

Contact persons: [Alexander Heilmeier](mailto:alexander.heilmeier@tum.de), [Tim Stahl](mailto:stahl@ftm.mw.tum.de).
classic-CandidateElimination 2.0.0
Candidate-Elimination Algorithm is a Machine Learning Algorithm that builds the version space from Specific Hypothesis and General Hypothesis.
Requires Python: >=3.0

Candidate-Elimination Algorithm
===================


Candidate-Elimination Algorithm is a Machine Learning Algorithm that builds the version space from Specific Hypothesis and General Hypothesis.

----------


Installation
-------------
Install directly from my [PyPi](https://pypi.org/project/classic-CandidateElimination/)

> pip install classic-CandidateElimination

Or Clone the [Repository](https://github.com/safir72347/ML-CandidateElimination-PyPi) and install

> python3 setup.py install

Parameters
-------------

## * X_train 
-------------
The Training Set array consisting of Features.

## * y_train
-------------
The Training Set array consisting of Outcome.


Attributes
-------------

## * fit(X_train, y_train)
-------------
Fit the Training Set to the model.

## * predict(y_test)
-------------
Predict the Test Set Results.



<i class="icon-file"></i> Documentation
-------------

### 1.  Install the package
>  pip install classic_FindS

### 2. Import the library
>  from classic_CandidateElimination import Candidate_Elimination

### 3. Create an object for FindS class
> ce = Candidate_Elimination()

### 4. Fit your Training Set to the model
> fs.fit(X_train, y_train)

### 5. Predict your Test Set results
> y_pred = fs.predict(y_test)

----------



Example Code
-------------

### 1. Import the dataset and Preprocess
> * import numpy as np
> * import pandas as pd
> * dataset = pd.read_csv('Covid-19_Data.csv')
> * result = {'Yes':1, 'No':0}
> * dataset['Covid_19'] = dataset['Covid_19'].map(result)
> * X = dataset.iloc[:, 0:5].values
> * y = dataset.iloc[:, -1].values

> * from sklearn.model_selection import KFold
> * kf = KFold(n_splits=10)
> * for train_index, test_index in kf.split(X,y):
>	 * X_train, X_test = X[train_index], X[test_index]
>	 * y_train, y_test = y[train_index], y[test_index]

### 2. Use the Find-S Library
> * from classic_CandidateElimination import Candidate_Elimination
> * ce = Candidate_Elimination()            
> * ce.fit(X_train, y_train)
> * y_pred = ce.predict(X_test) 


----------



Footnotes
-------------

You can find the code at my [Github](https://github.com/safir72347/ML-CandidateElimination-PyPi).



Connect with me on Social Media
-------------

* [https://www.github.com/safir72347](www.github.com/safir72347)
* [https://www.linkedin.com/in/safir72347/](https://www.linkedin.com/in/safir72347/)
* [https://www.instagram.com/safir_12_10/](https://www.instagram.com/safir_12_10/)
classic-FindS 2.0.0
Find-S algorithm is a Machine Learning Algorithm that finds the most specific hypothesis that fits all the positive examples.
Requires Python: >=3.0

Find-S Algorithm
===================


Find-S algorithm is a Machine Learning Algorithm that finds the most specific hypothesis that fits all the positive examples.

----------

Installation
-------------
Install directly from my [PyPi](https://pypi.org/project/classic-FindS/)

> pip install classic-FindS

Or Clone the [Repository](https://github.com/safir72347/ML-FindS-PyPi) and install

> python3 setup.py install

Parameters
-------------

## * X_train 
-------------
The Training Set array consisting of Features.

## * y_train
-------------
The Training Set array consisting of Outcome.


Attributes
-------------

## * fit(X_train, y_train)
-------------
Fit the Training Set to the model.

## * predict(y_test)
-------------
Predict the Test Set Results.



<i class="icon-file"></i> Documentation
-------------

### 1.  Install the package
>  pip install classic_FindS

### 2. Import the library
>  from classic_FindS import FindS

### 3. Create an object for FindS class
> fs = FindS()

### 4. Fit your Training Set to the model
> fs.fit(X_train, y_train)

### 5. Predict your Test Set results
> y_pred = fs.predict(y_test)

----------



Example Code
-------------

### 1. Import the dataset and Preprocess
> * import numpy as np
> * import pandas as pd
> * dataset = pd.read_csv('Covid-19_Data.csv')
> * result = {'Yes':1, 'No':0}
> * dataset['Covid_19'] = dataset['Covid_19'].map(result)
> * X = dataset.iloc[:, 0:5].values
> * y = dataset.iloc[:, -1].values

> * from sklearn.model_selection import KFold
> * kf = KFold(n_splits=10)
> * for train_index, test_index in kf.split(X,y):
>	 * X_train, X_test = X[train_index], X[test_index]
>	 * y_train, y_test = y[train_index], y[test_index]

### 2. Use the Find-S Library
> * from classic_FindS import FindS
> * fs = FindS()            
> * S_hypothesis = fs.fit(X_train, y_train)
> * print("Specific Hypothesis : ", S_hypothesis)
> * y_pred = fs.predict(X_test) 


----------



Footnotes
-------------

You can find the code at my [Github](https://github.com/safir72347/ML-FindS-PyPi).



Connect with me on Social Media
-------------

* [https://www.github.com/safir72347](www.github.com/safir72347)
* [https://www.linkedin.com/in/safir72347/](https://www.linkedin.com/in/safir72347/)
* [https://www.instagram.com/safir_12_10/](https://www.instagram.com/safir_12_10/)