============
Installation
============

* Install the `current PyPI release <https://pypi.python.org/pypi/xanfis />`_::

   $ pip install xanfis==1.1.0


* Install directly from source code::

   $ git clone https://github.com/thieu1995/X-ANFIS.git
   $ cd X-ANFIS
   $ python setup.py install

* In case, you want to install the development version from Github::

   $ pip install git+https://github.com/thieu1995/X-ANFIS


After installation, you can import MetaPerceptron as any other Python module::

   $ python
   >>> import xanfis
   >>> xanfis.__version__

========
Examples
========

In this section, we will explore the usage of the Adam-based Gradient Optimizer for training ANFIS networks::

	from xanfis import Data, GdAnfisClassifier
    from sklearn.datasets import load_breast_cancer


    ## Load data object
    X, y = load_breast_cancer(return_X_y=True)
    data = Data(X, y)

    ## Split train and test
    data.split_train_test(test_size=0.2, random_state=2, inplace=True, shuffle=True)
    print(data.X_train.shape, data.X_test.shape)

    ## Scaling dataset
    data.X_train, scaler_X = data.scale(data.X_train, scaling_methods=("standard", "minmax"))
    data.X_test = scaler_X.transform(data.X_test)

    data.y_train, scaler_y = data.encode_label(data.y_train)
    data.y_test = scaler_y.transform(data.y_test)

    print(type(data.X_train), type(data.y_train))

    ## Create model
    model = GdAnfisClassifier(num_rules=20, mf_class="Trapezoidal",
                              act_output=None, vanishing_strategy="blend", reg_lambda=None,
                              epochs=100, batch_size=16, optim="Adam", optim_params={"lr": 0.01},
                              early_stopping=True, n_patience=10, epsilon=0.001, valid_rate=0.1,
                              seed=42, verbose=True)
    ## Train the model
    model.fit(X=data.X_train, y=data.y_train)

    ## Test the model
    y_pred = model.predict(data.X_test)
    print(y_pred)
    print(model.predict_proba(data.X_test))

    ## Calculate some metrics
    print(model.evaluate(y_true=data.y_test, y_pred=y_pred, list_metrics=["F2S", "CKS", "FBS", "PS", "RS", "NPV", "F1S"]))


A real-world dataset contains features that vary in magnitudes, units, and range. We would suggest performing
normalization when the scale of a feature is irrelevant or misleading. Feature Scaling basically helps to normalize
the data within a particular range.

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