tick.linear_model.SimuLinReg

class tick.linear_model.SimuLinReg(weights: numpy.ndarray, intercept: float = None, features: numpy.ndarray = None, n_samples: int = 200, std: float = 1.0, features_type: str = 'cov_toeplitz', cov_corr: float = 0.5, features_scaling: str = 'none', seed: int = None, verbose: bool = True, dtype='float64')[source]

Simulation of a Linear 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

std : float, default=1.

Standard deviation of the noise (Gaussian)

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

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

dtype : {'float64', 'float32'}, default=’float64’

Type of the generated arrays. Used in the case features is None

__init__(weights: numpy.ndarray, intercept: float = None, features: numpy.ndarray = None, n_samples: int = 200, std: float = 1.0, features_type: str = 'cov_toeplitz', cov_corr: float = 0.5, features_scaling: str = 'none', seed: int = None, verbose: bool = True, dtype='float64')[source]

Initialize self. See help(type(self)) for accurate signature.

simulate()[source]

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