cuvis_ai.supervised.sklearn_wrapped.QDA

class cuvis_ai.supervised.sklearn_wrapped.QDA(*, priors=None, reg_param=0.0, store_covariance=False, tol=0.0001)[source]

__init__(*, priors=None, reg_param=0.0, store_covariance=False, tol=0.0001)

Methods

`__init__`(*[, priors, reg_param, ...])
`check_input_dim`(X)	Check that the parameters for the input data data match user expectations
`check_output_dim`(X)	Check that the parameters for the output data data match user expectations
`fit`(X, Y[, warm_start])
`forward`(X)	Transform the input data.
`get_fit_requested_meta`()
`get_forward_requested_meta`()
`load`(params, data_dir)	Load from serialized format into an object
`serialize`(data_dir)	Convert the class into a serialized representation
`set_fit_meta_request`(**kwargs)
`set_forward_meta_request`(**kwargs)

Attributes

`input_dim`	Returns the needed shape for the input data.
`output_dim`	Returns the shape for the output data.

check_input_dim(X)

Check that the parameters for the input data data match user expectations

Parameters: X (array-like): Input data.

Returns: (Bool) Valid data

check_output_dim(X)

Check that the parameters for the output data data match user expectations

Parameters: X (array-like): Input data.

Returns: (Bool) Valid data

forward(X: ndarray)

Transform the input data.

Parameters: X (array-like): Input data.

Returns: Transformed data.

property input_dim

Returns the needed shape for the input data. If a dimension is not important, it will return -1 in the specific position.

Returns: (tuple) needed shape for data

load(params: dict, data_dir: Path) → None: Load from serialized format into an object

property output_dim

Returns the shape for the output data. If a dimension is dependent on the input, it will return -1 in the specific position.

Returns: (tuple) expected output shape for data

serialize(data_dir: Path) → dict: Convert the class into a serialized representation