text: """
  .. _example8:
  
  Eighth Example: # Dynamically built component using input signal values during the fit stage
  --------------------------------------------------------------------------------------------
  
  We continue demonstrating several interesting features:
   1. How the user can choose to encapsulate several blocks into a PipeGraph and use it as a single unit in another PipeGraph
   2. How these components can be dynamically built on runtime depending on initialization parameters
   3. How these components can be dynamically built on runtime depending on input signal values during fit
   4. Using GridSearchCV to explore the best combination of hyperparameters
  
  """
  import numpy as np
  import pandas as pd
  import matplotlib.pyplot as plt
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.mixture import GaussianMixture
  
  from sklearn.linear_model import LinearRegression
  from pipegraph.base import PipeGraph, RegressorsWithDataDependentNumberOfReplicas
  
  X_first = pd.Series(np.random.rand(100,))
  y_first = pd.Series(4 * X_first + 0.5*np.random.randn(100,))
  X_second = pd.Series(np.random.rand(100,) + 3)
  y_second = pd.Series(-4 * X_second + 0.5*np.random.randn(100,))
  X_third = pd.Series(np.random.rand(100,) + 6)
  y_third = pd.Series(2 * X_third + 0.5*np.random.randn(100,))
  
  X = pd.concat([X_first, X_second, X_third], axis=0).to_frame()
  y = pd.concat([y_first, y_second, y_third], axis=0).to_frame()
  
  #######################################################################################################
  # Now we consider the possibility of using the classifier's output to automatically adjust the number of replicas.
  # This can be seen as PipeGraph changing its inner topology to adapt its connections and steps to other components
  # context. This morphing capability opens interesting possibilities to explore indeed.
  
  import inspect
  print(inspect.getsource(RegressorsWithDataDependentNumberOfReplicas))
  
  ################################################################################################
  # Using this new component we can build a simplified PipeGraph:
  
  scaler = MinMaxScaler()
  gaussian_mixture = GaussianMixture(n_components=3)
  models = RegressorsWithDataDependentNumberOfReplicas()
  
  steps = [('scaler', scaler),
           ('classifier', gaussian_mixture),
           ('models', models), ]
  
  connections = {'scaler': {'X': 'X'},
                 'classifier': {'X': 'scaler'},
                 'models': {'X': 'scaler',
                            'y': 'y',
                            'selection': 'classifier'},
                 }
  
  pgraph = PipeGraph(steps=steps, fit_connections=connections)
  pgraph.fit(X, y)
  y_pred = pgraph.predict(X)
  plt.scatter(X, y)
  plt.scatter(X, y_pred)
  