How to use the mlxtend._base._Classifier function in mlxtend

# Sebastian Raschka 2014-2019
# mlxtend Machine Learning Library Extensions
#
# Implementation of the logistic regression algorithm for classification.
# Author: Sebastian Raschka 
#
# License: BSD 3 clause

import numpy as np
from time import time
from .._base import _BaseModel
from .._base import _IterativeModel
from .._base import _Classifier


class LogisticRegression(_BaseModel, _IterativeModel, _Classifier):

    """Logistic regression classifier.

    Note that this implementation of Logistic Regression
    expects binary class labels in {0, 1}.

    Parameters
    ------------
    eta : float (default: 0.01)
        Learning rate (between 0.0 and 1.0)
    epochs : int (default: 50)
        Passes over the training dataset.
        Prior to each epoch, the dataset is shuffled
        if `minibatches > 1` to prevent cycles in stochastic gradient descent.
    l2_lambda : float
        Regularization parameter for L2 regularization.

# Sebastian Raschka 2014-2019
# mlxtend Machine Learning Library Extensions
#
# Implementation of the ADAptive LInear NEuron classification algorithm.
# Author: Sebastian Raschka 
#
# License: BSD 3 clause

import numpy as np
from time import time
from .._base import _BaseModel
from .._base import _IterativeModel
from .._base import _Classifier


class Adaline(_BaseModel, _IterativeModel, _Classifier):

    """ADAptive LInear NEuron classifier.

    Note that this implementation of Adaline expects binary class labels
    in {0, 1}.

    Parameters
    ------------
    eta : float (default: 0.01)
        solver rate (between 0.0 and 1.0)
    epochs : int (default: 50)
        Passes over the training dataset.
        Prior to each epoch, the dataset is shuffled
        if `minibatches > 1` to prevent cycles in stochastic gradient descent.
    minibatches : int (default: None)
        The number of minibatches for gradient-based optimization.

# Sebastian Raschka 2014-2019
# mlxtend Machine Learning Library Extensions
#
# Implementation of Rosenblatt's perceptron algorithm for classification.
# Author: Sebastian Raschka 
#
# License: BSD 3 clause

import numpy as np
from time import time
from .._base import _BaseModel
from .._base import _IterativeModel
from .._base import _Classifier


class Perceptron(_BaseModel, _IterativeModel, _Classifier):

    """Perceptron classifier.

    Note that this implementation of the Perceptron expects binary class labels
    in {0, 1}.

    Parameters
    ------------
    eta : float (default: 0.1)
        Learning rate (between 0.0 and 1.0)
    epochs : int (default: 50)
        Number of passes over the training dataset.
        Prior to each epoch, the dataset is shuffled to prevent cycles.
    random_seed : int
        Random state for initializing random weights and shuffling.
    print_progress : int (default: 0)

# Implementation of Softmax Regression in Tensorflow
# Author: Sebastian Raschka 
#
# License: BSD 3 clause

import tensorflow as tf
import numpy as np
from time import time
from .._base import _BaseModel
from .._base import _IterativeModel
from .._base import _MultiClass
from .._base import _Classifier


class TfSoftmaxRegression(_BaseModel, _IterativeModel, _MultiClass,
                          _Classifier):
    """Softmax regression classifier.

    Parameters
    ------------
    eta : float (default: 0.5)
        Learning rate (between 0.0 and 1.0)
    epochs : int (default: 50)
        Passes over the training dataset.
        Prior to each epoch, the dataset is shuffled
        if `minibatches > 1` to prevent cycles in stochastic gradient descent.
    n_classes : int (default: None)
        A positive integer to declare the number of class labels
        if not all class labels are present in a partial training set.
        Gets the number of class labels automatically if None.
    minibatches : int (default: 1)
        Divide the training data into *k* minibatches