class_docstr: Simulation of a Logistic regression model
  
  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
  
  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
docstr: Launch simulation of the data
  
  Returns
  -------
  features : `numpy.ndarray`, shape=(n_samples, n_features)
      The features matrix
  
  labels : `numpy.ndarray`, shape=(n_samples,)
      The labels vector
docstr_tok: ["Launch","simulation","of","the","data","Returns","--","--","--","-","features",":","`","numpy.ndarray","`",",","shape=","(","n_samples",",","n_features",")","The","features","matrix","labels",":","`","numpy.ndarray","`",",","shape=","(","n_samples",",",")","The","labels","vector"]
code: def simulate(self):
      """
      Launch simulation of the data
  
      Returns
      -------
      features : `numpy.ndarray`, shape=(n_samples, n_features)
          The features matrix
  
      labels : `numpy.ndarray`, shape=(n_samples,)
          The labels vector
      """
      return SimuWithFeatures.simulate(self)
  
imports: import numpy
  from tick.base.simulation import SimuWithFeatures
label_desc: Use image node_id 2 for calling the SimuLogReg obj's underlying member method code with example usage: obj.simulate() and returns: SimuWithFeatures