fuzzy-rough-learn API¶
This is the full API documentation of fuzzy-rough-learn.
Classifiers¶
Implementation of Fuzzy Rough Nearest Neighbour (FRNN) classification. |
|
Implementation of the Fuzzy Rough OVO COmbination (FROVOCO) ensemble classifier [Rcffc929d1d8e-1]. |
|
Implementation of the Fuzzy ROugh NEighbourhood Consensus (FRONEC) multilabel classifier. |
Data descriptors¶
Implementation of the Average Localised Proximity (ALP) data descriptor [R4d6affda87e1-1]. |
|
Implementation of the Centre Distance (CD) data descriptor. |
|
Wrapper for the Isolation Forest (IF) data descriptor [R288ff40200ea-1] implemented in scikit-learn. |
|
Implementation of the Localised Nearest Neighbour Distance (LNND) data descriptor [Rdc09924e667a-1]_[Rdc09924e667a-2]_. |
|
Implementation of the Local Outlier Factor (LOF) data descriptor [Race47188aa49-1]. |
|
Implementation of the Mahalanobis Distance (MD) data descriptor [Rddee2b422011-1]. |
|
Implementation of the Nearest Neighbour Distance (NND) data descriptor, which goes back to at least [R2f1514802a03-1]. |
|
Wrapper for the Support Vector Machine (SVM) data descriptor [R52cd54939bff-1] with gaussian kernel, implemented in scikit-learn. |
Regressors¶
Implementation of the Fuzzy Rough Nearest Neighbour (FRNN) regressor [R889f5cf58f8d-1]. |
Feature preprocessors¶
Linear normalisers¶
Linearly transforms all features by normalising a measure of dispersion and a measure of location, ensuring that for each feature, that measure of dispersion becomes 1 and that measure of location becomes 0. |
|
Implementation of the interquartile range (IQR) normaliser. |
|
Implementation of the maximum absolute value normaliser. |
|
Implementation of the range normaliser. |
|
Implementation of the standard deviation normaliser, or standardiser. |
Other¶
Implementation of the Fuzzy Rough Feature Selection (FRFS) preprocessor. |
|
Rescales each instance (seen as a vector) to a fixed size. |
Instance preprocessors¶
Implementation of the Fuzzy Rough Prototype Selection (FRPS) preprocessor. |
Other¶
Array functions¶
Divides x by y, replacing np.nan values with fallback. |
|
Returns the k first values of a along the specified axis. |
|
Returns the k greatest values of a along the specified axis, in order. |
|
Returns the k last values of a along the specified axis, in reverse order. |
|
Returns the k least values of a along the specified axis, in order. |
|
Remove the diagonal from a square array. |
|
Calculates the soft head of an array. |
|
Calculates the soft maximum of an array. |
|
Calculates the soft minimum of an array. |
|
Calculates the soft tail of an array. |
Dispersion measures¶
Distance between the first and the third quartile; range of the central half of the data. |
|
Maximum distance from 0. |
|
Square root of the sum of the squared distances to the mean. |
|
Distance between the smallest and largest value. |
Location measures¶
Greatest value. |
|
Sum of all values divided by the number of values. |
|
Middle value after sorting all values by size, or mean of the two middle values. |
|
Mean of the first and third quartiles. |
|
Mean of the minimum and maximum. |
|
Least value. |
Nearest neighbour search algorithms¶
Nearest neighbour search with a Ball tree. |
|
Nearest neighbour search with a KD-tree. |
Parametrisations¶
Function to obtain multiples of the logarithm of other numbers. |
|
Function to obtain multiples of other numbers. |
T-norms¶
x * y; also known as product t-norm. |
|
min(x, y); also known as Gödel or minimum t-norm. |
|
max(x + y - 1, 0) |
Transformations¶
Strictly order-preserving function from [-∞, ∞] to [0, 1] that sends -∞, -c, 0, c, ∞ to 0, 0.25, 0.5, 0.75, 1, respectively. |
|
Order-reversing function from [0, ∞) to [0, 1] that sends x to 1/(1 + x/c). |
|
Order-reversing function from [0, ∞) to [0, 1] that sends x to max(0, 1 - x). |
Vector size measures¶
Family of vector size measures of the form `(x1**p + x2**p + . |
Weights¶
Abstract base class for parametrisable weights functions. |
|
(1/4, 1/4, 1/4, 1/4) Also known as mean weights, as they compute the unweighted mean. |
|
(8/15, 4/15, 2/15, 1/15) Exponentially decreasing weights with parametrisable base. |
|
(4/10, 3/10, 2/10, 1/10) Also known as additive weights. |
|
Weights that encode a regular non-decreasing quantifier [Rcc56311c3f1f-1]. |
|
(12/25, 12/50, 12/75, 12/100) Also known as inverse additive weights. |