Metadata-Version: 2.1
Name: gmdh
Version: 1.0
Summary: README.md
Home-page: https://github.com/bauman-team/GMDH
Author: Artem Babin
Author-email: artem031201@gmail.com
License: LICENSE.md
Project-URL: Documentation, https://bauman-team.github.io/GMDH/python/html/
Project-URL: GMDH book, https://bauman-team.github.io/GMDH_book
Project-URL: Source Code, https://github.com/bauman-team/GMDH
Description: # Group Method of Data Hahdling (GMDH) - the family of deep learning algorithms.
        
        ![][c++-shield] ![][boost-shield] ![][pybind-shield] ![][python-shield] ![][eigen-shield]
        
        GMDH is a Python module that implements algorithms for the group method of data handling.
        
        Read the Python module [documentation](https://bauman-team.github.io/GMDH/python/html/).
        ## About GMDH
        
        GMDH is a machine learning Python module (API) based on C++ library for fast calculations. It realized the Group Method of Data Handling. It is a set of several algorithms for different machine learning tasks solution.
        
        It was developed with a focus on providing fast experimentations and studing.
        
        The gmdh module implements 4 popular varieties of algorithms from the family of GMDH algorithms (COMBI, MULTI, MIA, RIA), designed to solve problems of data approximation and time series prediction. The library also includes auxiliary functionality for basic data preprocessing and saving already trained models.
        
        ## Short theory
        
        Group Method of Data Handling was applied in a great variety of areas for deep learning and knowledge discovery, forecasting and data mining, optimization and pattern recognition.
        Inductive GMDH algorithms give possibility to find automatically interrelations in data, to select an optimal structure of model or network and to increase the accuracy of existing algorithms.
        
        You can read the detailed theory at [gmdh.net](https://gmdh.net/index.html).
        
        ---
        
        ## Installation
        
        To install gmdh package you need run command:
        
        ```
        pip install gmdh
        ```
        Using:
        ```python
        import gmdh
        ```
        
        ---
        
        ## First contact with gmdh
        
        Let's consider the simplest example of using the basic combinatorial COMBI algorithm from the gmdh module.
        
        To begin with, we import the Combi model and the split_data function from the module to split the source data into training and test samples:
        ```python
        from gmdh import Combi, split_data
        ```
        
        Let's create a simple dataset in which the target values of the matrix `y` will simply be the sum of the corresponding pair of values `x1` and `x2` of the matrix `X`:
        ```python
        X = [[1, 2], [3, 2], [7, 0], [5, 5], [1, 4], [2, 6]]
        y = [3, 5, 7, 10, 5, 8]
        ```
        
        Let's divide our data into training and test samples:
        ```python
        x_train, x_test, y_train, y_test = split_data(X, y)
        
        # print result arrays
        print('x_train:\n', x_train)
        print('x_test:\n', x_test)
        print('\ny_train:\n', y_train)
        print('y_test:\n', y_test)
        ```
        Output:
        ```
        x_train:
         [[1. 2.]
         [3. 2.]
         [7. 0.]
         [5. 5.]
         [1. 4.]]
        x_test:
         [[2. 6.]]
        
        y_train:
         [ 3.  5.  7. 10.  5.]
        y_test:
         [8.]
        ```
        
        Let's create a `Combi` model, train it using training data by the `fit` method and then predict the result for the test sample using the `predict` method:
        ```python
        model = Combi()
        model.fit(x_train, y_train)
        y_predicted = model.predict(x_test)
        
        # compare predicted and real value
        print('y_predicted: ', y_predicted)
        print('y_test: ', y_test)
        ```
        
        Output:
        ```
        y_predicted:  [8.]
        y_test:  [8.]
        ```
        
        The predicted result coincided with the real value! Now we will output a polynomial that displays the pattern found by the model:
        ```python
        model.get_best_polynomial()
        ```
        
        Output:
        ```
        'y = x1 + x2'
        ```
        
        For more in-depth tutorials about gmdh you can check our online [GMDH_book](https://bauman-team.github.io/GMDH_book/intro.html).
        
        ---
        ## License
        
        This project is licensed under the [Apache 2.0](https://github.com/bauman-team/GMDH/blob/master/LICENSE.md) License.
        
        --- 
        ## Release notes
        
        This is a bachelor's diploma project. It was written by students of the Bauman Moscow State Technical University (BMSTU). The first version 1.0 is released in PyPI. This version is the final one for the graduation project, but the project itself and the repository can continue to grow and improve. We will be glad to new ideas and suggestions.
        
        All the release branches can be found on [GitHub](https://github.com/bauman-team/GMDH/releases).
        
        All the release binaries can be found on [PyPI](https://pypi.org/project/gmdh/#history).
        
        
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Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Information Technology
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: Unix
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: C++
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=3.6
Description-Content-Type: text/markdown
