sklearn 0.22-0.2.0 · OCaml Package

type t

val of_pyobject : Py.Object.t -> t

val to_pyobject : t -> Py.Object.t

val create : 
  ?patch_size:Py.Object.t ->
  ?max_patches:[ `I of int | `F of float ] ->
  ?random_state:int ->
  unit ->
  t

Extracts patches from a collection of images

Read more in the :ref:`User Guide <image_feature_extraction>`.

.. versionadded:: 0.9

Parameters ---------- patch_size : tuple of ints (patch_height, patch_width) the dimensions of one patch

max_patches : integer or float, optional default is None The maximum number of patches per image to extract. If max_patches is a float in (0, 1), it is taken to mean a proportion of the total number of patches.

random_state : int, RandomState instance or None, optional (default=None) Determines the random number generator used for random sampling when `max_patches` is not None. Use an int to make the randomness deterministic. See :term:`Glossary <random_state>`.

Examples -------- >>> from sklearn.datasets import load_sample_images >>> from sklearn.feature_extraction import image >>> # Use the array data from the second image in this dataset: >>> X = load_sample_images().images1 >>> print('Image shape: {

}

'.format(X.shape)) Image shape: (427, 640, 3) >>> pe = image.PatchExtractor(patch_size=(2, 2)) >>> pe_fit = pe.fit(X) >>> pe_trans = pe.transform(X) >>> print('Patches shape: {

}

'.format(pe_trans.shape)) Patches shape: (545706, 2, 2)

val fit : ?y:Py.Object.t -> x:Arr.t -> t -> t

Do nothing and return the estimator unchanged

This method is just there to implement the usual API and hence work in pipelines.

Parameters ---------- X : array-like, shape n_samples, n_features Training data.

val get_params : ?deep:bool -> t -> Dict.t

Get parameters for this estimator.

Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns ------- params : mapping of string to any Parameter names mapped to their values.

val set_params : ?params:(string * Py.Object.t) list -> t -> t

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.

Parameters ---------- **params : dict Estimator parameters.

Returns ------- self : object Estimator instance.

val transform : x:Arr.t -> t -> Arr.t

Transforms the image samples in X into a matrix of patch data.

Parameters ---------- X : array, shape = (n_samples, image_height, image_width) or (n_samples, image_height, image_width, n_channels) Array of images from which to extract patches. For color images, the last dimension specifies the channel: a RGB image would have `n_channels=3`.

Returns ------- patches : array, shape = (n_patches, patch_height, patch_width) or (n_patches, patch_height, patch_width, n_channels) The collection of patches extracted from the images, where `n_patches` is either `n_samples * max_patches` or the total number of patches that can be extracted.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Format.formatter -> t -> unit

Pretty-print the object to a formatter.

package sklearn