class_docstr: Simulation of a Poisson regression model, with
  identity or exponential link.
  
  Parameters
  ----------
  weights : `numpy.ndarray`, shape=(n_features,)
      The array of weights of the model
  
  intercept : `float`, default=`None`
      The intercept. If None, then no intercept is used
  
  features : `numpy.ndarray`, shape=(n_samples, n_features), default=`None`
      The features matrix to use. If None, it is simulated
  
  n_samples : `int`, default=200
      Number of samples
  
  link : `str`, default="exponential"
      Type of link function
  
      * if ``"identity"`` : the intensity is the inner product of the
        model's weights with the features. In this case, one must
        ensure that the intensity is non-negative
  
      * if ``"exponential"`` : the intensity is the exponential of the
        inner product of the model's weights with the features
  
  features_type : `str`, default="cov_toeplitz"
      The type of features matrix to simulate
  
      * If ``"cov_toeplitz"`` : a Gaussian distribution with
        Toeplitz correlation matrix
  
      * If ``"cov_uniform"`` : a Gaussian distribution with
        correlation matrix given by .5 * (U + U.T), where U is
        uniform on [0, 1] and diagonal filled with ones.
  
  cov_corr : `float`, default=.5
      Correlation to use in the Toeplitz correlation matrix
  
  features_scaling : `str`, default="none"
      The way the features matrix is scaled after simulation
  
      * If ``"standard"`` : the columns are centered and
        normalized
  
      * If ``"min-max"`` : remove the minimum and divide by
        max-min
  
      * If ``"norm"`` : the columns are normalized but not centered
  
      * If ``"none"`` : nothing is done to the features
  
  seed : `int`, default=None
      The seed of the random number generator. If `None` it is not
      seeded
  
  verbose : `bool`, default=True
      If `True`, print things
  
  dtype : `{'float64', 'float32'}`, default='float64'
      Type of the generated arrays.
      Used in the case features is None
  
  Attributes
  ----------
  features : `numpy.ndarray`, shape=(n_samples, n_features)
      The simulated (or given) features matrix
  
  labels : `numpy.ndarray`, shape=(n_samples,)
      The simulated labels
  
  time_start : `str`
      Start date of the simulation
  
  time_elapsed : `int`
      Duration of the simulation, in seconds
  
  time_end : `str`
      End date of the simulation
code: def link(self):
      return self._link
  
file_path: tick/tick/linear_model/simu_poisreg.py
imports: import numpy
  from numpy.random.mtrand import poisson
  from os import linesep
  from warnings import warn
  from tick.base.simulation import SimuWithFeatures
label_desc: Use image node_id 2 for calling the SimuPoisReg obj's underlying member method code with example usage: obj.link() and returns: self