gaussian#
Refactoring of sklearn.gaussian_process.GaussianProcessClassifier to allow for iterative training
- class perceptivo.psychophys.gaussian._IterativeBinaryGPCLaplace(kernel=None, *, optimizer='fmin_l_bfgs_b', n_restarts_optimizer=0, max_iter_predict=100, warm_start=False, copy_X_train=True, random_state=None)#
Bases:
sklearn.gaussian_process._gpc._BinaryGaussianProcessClassifierLaplaceReclassing to allow for fitting without needing a sample with >=2 categories
- fit(X, y)#
Fit Gaussian process classification model.
- Parameters
X – array-like of shape (n_samples, n_features) or list of object Feature vectors or other representations of training data.
y – array-like of shape (n_samples,) Target values, must be binary.
- Returns
self
- Return type
returns an instance of self.
- class perceptivo.psychophys.gaussian.IterativeGPC(kernel=None, *, optimizer='fmin_l_bfgs_b', n_restarts_optimizer=0, max_iter_predict=100, warm_start=False, copy_X_train=True, random_state=None, multi_class='one_vs_rest', n_jobs=None)#
Bases:
sklearn.gaussian_process._gpc.GaussianProcessClassifierReclassed to use patched
_IterativeBinaryGPCLaplaceinstead of original model- fit(X, y)#
Fit Gaussian process classification model.
- Parameters
X – array-like of shape (n_samples, n_features) or list of object Feature vectors or other representations of training data.
y – array-like of shape (n_samples,) Target values, must be binary.
- Returns
self
- clone_kernel() sklearn.gaussian_process.kernels.Kernel#