class_docstr: Kohonen Neural Network used for unsupervised learning.
  
  Parameters
  ----------
  {BaseAssociative.Parameters}
  
  Methods
  -------
  {BaseAssociative.Methods}
  
  Examples
  --------
  >>> import numpy as np
  >>> from neupy import algorithms, layers, utils
  >>>
  >>> utils.reproducible()
  >>>
  >>> X = np.array([
  ...     [0.1961,  0.9806],
  ...     [-0.1961,  0.9806],
  ...     [0.9806,  0.1961],
  ...     [0.9806, -0.1961],
  ...     [-0.5812, -0.8137],
  ...     [-0.8137, -0.5812],
  ... ])
  >>>
  >>> kohonet = algorithms.Kohonen(
  ...     n_inputs=2,
  ...     n_outputs=3,
  ...     step=0.5,
  ...     verbose=False
  ... )
  >>> kohonet.train(X, epochs=100)
  >>> kohonet.predict(X)
  array([[ 0.,  1.,  0.],
         [ 0.,  1.,  0.],
         [ 1.,  0.,  0.],
         [ 1.,  0.,  0.],
         [ 0.,  0.,  1.],
         [ 0.,  0.,  1.]])
code: def predict(self, X):
      raw_output = self.predict_raw(X)
      output = np.zeros(raw_output.shape, dtype=np.int0)
      max_args = raw_output.argmax(axis=1)
      output[range(raw_output.shape[0]), max_args] = 1
      return output
  
file_path: neupy/neupy/algorithms/associative/kohonen.py
imports: import numpy
  from neupy.utils import format_data
  from .base import BaseAssociative
label_desc: Use image node_id 2 for calling the Kohonen obj's underlying member method code with example usage: obj.predict(X) and returns: output