skdim.id.TwoNN¶
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class
skdim.id.TwoNN(discard_fraction: float = 0.1, dist: bool = False)[source]¶ Intrinsic dimension estimation using the TwoNN algorithm. [Facco2019] [IDFacco] [IDMottes]
Parameters: -
x_¶ np.array with the -log(mu) values.
Type: 1d array
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y_¶ np.array with the -log(F(mu_{sigma(i)})) values.
Type: 1d array
Methods
fit(X[, y])A reference implementation of a fitting function. fit_pw(X[, precomputed_knn, smooth, …])Creates an array of pointwise ID estimates (self.dimension_pw_) by fitting the estimator in kNN of each point. fit_transform(X[, y])Fit estimator and return ID fit_transform_pw(X[, precomputed_knn, …])Returns an array of pointwise ID estimates by fitting the estimator in kNN of each point. get_params([deep])Get parameters for this estimator. set_params(**params)Set the parameters of this estimator. transform([X])Predict dimension after a previous call to self.fit transform_pw([X])Return an array of pointwise ID estimates after a previous call to self.fit_pw -
fit(X, y=None)[source]¶ A reference implementation of a fitting function. :param X: A data set for which the intrinsic dimension is estimated. :type X: {array-like}, shape (n_samples, n_features) :param y: :type y: dummy parameter to respect the sklearn API
Returns: self (object) – Returns self.
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fit_pw(X, precomputed_knn=None, smooth=False, n_neighbors=100, n_jobs=1)¶ Creates an array of pointwise ID estimates (self.dimension_pw_) by fitting the estimator in kNN of each point.
Parameters: - X : np.array (n_samples x n_neighbors)
Dataset to fit
- precomputed_knn : np.array (n_samples x n_dims)
An array of precomputed (sorted) nearest neighbor indices
- n_neighbors
Number of nearest neighbors to use (ignored when using precomputed_knn)
- n_jobs : int
Number of processes
- smooth : bool, default = False
- Additionally computes a smoothed version of pointwise estimates by
taking the ID of a point as the average ID of each point in its neighborhood (self.dimension_pw_)
Returns: self (object) – Returns self
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fit_transform(X, y=None)¶ Fit estimator and return ID
Parameters: - X : {array-like}, shape (n_samples, n_features)
The training input samples.
Returns: dimension_ ({int, float}) – The estimated intrinsic dimension
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fit_transform_pw(X, precomputed_knn=None, smooth=False, n_neighbors=100, n_jobs=1)¶ Returns an array of pointwise ID estimates by fitting the estimator in kNN of each point.
Parameters: - X : np.array (n_samples x n_neighbors)
Dataset to fit
- precomputed_knn : bool
An array of precomputed (sorted) nearest neighbor indices
- n_neighbors
Number of nearest neighbors to use (ignored when using precomputed_knn)
- n_jobs : int
Number of processes
- smooth : bool, default = False
- Additionally computes a smoothed version of pointwise estimates by
taking the ID of a point as the average ID of each point in its neighborhood (self.dimension_pw_)
Returns: - dimension_pw_ (np.array with dtype {int, float}) – Pointwise ID estimates
- dimension_pw_smooth_ (np.array with dtype float) – Smoothed pointwise ID estimates returned if self.fit_pw(smooth=True)
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get_params(deep=True)¶ 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 (dict) – Parameter names mapped to their values.
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set_params(**params)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline). 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 (estimator instance) – Estimator instance.
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transform(X=None)¶ Predict dimension after a previous call to self.fit
Parameters: - X : Dummy parameter
Returns: dimension_ ({int, float}) – The estimated ID
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transform_pw(X=None)¶ Return an array of pointwise ID estimates after a previous call to self.fit_pw
Parameters: - X : Dummy parameter
Returns: - dimension_pw_ (np.array with dtype {int, float}) – Pointwise ID estimates
- dimension_pw_smooth_ (np.array with dtype float) – Smoothed pointwise ID estimates returned if self.fit_pw(smooth=True)
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