Regression Models#

class regmmd.models.regression.GammaRegression(par_v=None, par_c=None, random_state=None)[source]#

score(X, y)[source]#

Evaluates to the gradient of the log likelihood with respect to the parameters at the values x.

Parameters:: x (ndarray of shape (n_samples, n_features), the points to be evaluated)

update(par_v)[source]#

Update the model with new parameters

class regmmd.models.regression.GammaRegressionLoc(par_v=None, par_c=None, random_state=None)[source]#

score(X, y)[source]#

Evaluates to the gradient of the log likelihood with respect to the parameters at the values x.

Parameters:: x (ndarray of shape (n_samples, n_features), the points to be evaluated)

update(par_v)[source]#

Update the model with new parameters

class regmmd.models.regression.LinearGaussian(par_v=None, par_c=None, random_state=None)[source]#

score(X, y)[source]#: gradient of the log-likelihood for each individual data point

update(par_v)[source]#

Update the model with new parameters

class regmmd.models.regression.LinearGaussianLoc(par_v=None, par_c=None, random_state=None)[source]#

score(X, y)[source]#: gradient of the log-likelihood for each individual data point

update(par_v)[source]#

Update the model with new parameters

class regmmd.models.regression.Logistic(par_v=None, par_c=None, random_state=None)[source]#

score(X, y)[source]#: gradient of the log-likelihood for each individual data point

update(par_v)[source]#

Update the model with new parameters

class regmmd.models.regression.PoissonRegression(par_v=None, par_c=None, random_state=None)[source]#