tick.linear_model.
SimuPoisReg
(weights: numpy.ndarray, intercept: float = None, features: numpy.ndarray = None, n_samples: int = 200, link: str = 'exponential', 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 Poisson regression model, with identity or exponential link.
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-negativeif
"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 matrixIf
"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 normalizedIf
"min-max"
: remove the minimum and divide by max-minIf
"norm"
: the columns are normalized but not centeredIf
"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
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
__init__
(weights: numpy.ndarray, intercept: float = None, features: numpy.ndarray = None, n_samples: int = 200, link: str = 'exponential', 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.