estimators¶
In this module, Standard TwinSVM and Least Squares TwinSVM estimators are defined.
Functions
rbf_kernel(x, y, u) |
It transforms samples into higher dimension using Gaussian (RBF) kernel. |
Classes
BaseTSVM(kernel, rect_kernel, C1, C2, gamma) |
Base class for TSVM-based estimators |
LSTSVM([kernel, rect_kernel, C1, C2, gamma, …]) |
Least Squares Twin Support Vector Machine (LSTSVM) for binary classification. |
TSVM([kernel, rect_kernel, C1, C2, gamma]) |
Standard Twin Support Vector Machine for binary classification. |
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class
estimators.BaseTSVM(kernel, rect_kernel, C1, C2, gamma)[source]¶ Bases:
sklearn.base.BaseEstimatorBase class for TSVM-based estimators
Parameters: kernel : str
Type of the kernel function which is either ‘linear’ or ‘RBF’.
rect_kernel : float
Percentage of training samples for Rectangular kernel.
C1 : float
Penalty parameter of first optimization problem.
C2 : float
Penalty parameter of second optimization problem.
gamma : float
Parameter of the RBF kernel function.
Attributes
mat_C_t (array-like, shape = [n_samples, n_samples]) A matrix that contains kernel values. cls_name (str) Name of the classifier. w1 (array-like, shape=[n_features]) Weight vector of class +1’s hyperplane. b1 (float) Bias of class +1’s hyperplane. w2 (array-like, shape=[n_features]) Weight vector of class -1’s hyperplane. b2 (float) Bias of class -1’s hyperplane. Methods
check_clf_params()Checks whether the estimator’s input parameters are valid. decision_function(X)Computes distance of test samples from both non-parallel hyperplanes fit(X, y)It fits a TSVM-based estimator. get_params([deep])Get parameters for this estimator. get_params_names()For retrieving the names of hyper-parameters of the TSVM-based estimator. predict(X)Performs classification on samples in X using the TSVM-based model. set_params(**params)Set the parameters of this estimator. -
get_params_names()[source]¶ For retrieving the names of hyper-parameters of the TSVM-based estimator.
Returns: parameters : list of str, {[‘C1’, ‘C2’], [‘C1’, ‘C2’, ‘gamma’]}
Returns the names of the hyperparameters which are same as the class’ attributes.
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fit(X, y)[source]¶ It fits a TSVM-based estimator. THIS METHOD SHOULD BE IMPLEMENTED IN CHILD CLASS.
Parameters: X : array-like, shape (n_samples, n_features)
Training feature vectors, where n_samples is the number of samples and n_features is the number of features.
y : array-like, shape(n_samples,)
Target values or class labels.
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class
estimators.TSVM(kernel='linear', rect_kernel=1, C1=1, C2=1, gamma=1)[source]¶ Bases:
estimators.BaseTSVMStandard Twin Support Vector Machine for binary classification. It inherits attributes of
BaseTSVM.Parameters: kernel : str, optional (default=’linear’)
Type of the kernel function which is either ‘linear’ or ‘RBF’.
rect_kernel : float, optional (default=1.0)
Percentage of training samples for Rectangular kernel.
C1 : float, optional (default=1.0)
Penalty parameter of first optimization problem.
C2 : float, optional (default=1.0)
Penalty parameter of second optimization problem.
gamma : float, optional (default=1.0)
Parameter of the RBF kernel function.
Methods
check_clf_params()Checks whether the estimator’s input parameters are valid. decision_function(X)Computes distance of test samples from both non-parallel hyperplanes fit(X_train, y_train)It fits the binary TwinSVM model according to the given training data. get_params([deep])Get parameters for this estimator. get_params_names()For retrieving the names of hyper-parameters of the TSVM-based estimator. predict(X)Performs classification on samples in X using the TSVM-based model. set_params(**params)Set the parameters of this estimator. -
fit(X_train, y_train)[source]¶ It fits the binary TwinSVM model according to the given training data.
Parameters: X_train : array-like, shape (n_samples, n_features)
Training feature vectors, where n_samples is the number of samples and n_features is the number of features.
y_train : array-like, shape(n_samples,)
Target values or class labels.
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class
estimators.LSTSVM(kernel='linear', rect_kernel=1, C1=1, C2=1, gamma=1, mem_optimize=False)[source]¶ Bases:
estimators.BaseTSVMLeast Squares Twin Support Vector Machine (LSTSVM) for binary classification. It inherits attributes of
BaseTSVM.Parameters: kernel : str, optional (default=’linear’)
Type of the kernel function which is either ‘linear’ or ‘RBF’.
rect_kernel : float, optional (default=1.0)
Percentage of training samples for Rectangular kernel.
C1 : float, optional (default=1.0)
Penalty parameter of first optimization problem.
C2 : float, optional (default=1.0)
Penalty parameter of second optimization problem.
gamma : float, optional (default=1.0)
Parameter of the RBF kernel function.
mem_optimize : boolean, optional (default=False)
If it’s True, it optimizes the memory consumption siginificantly. However, the memory optimization increases the CPU time.
Methods
check_clf_params()Checks whether the estimator’s input parameters are valid. decision_function(X)Computes distance of test samples from both non-parallel hyperplanes fit(X, y)It fits the binary Least Squares TwinSVM model according to the given training data. get_params([deep])Get parameters for this estimator. get_params_names()For retrieving the names of hyper-parameters of the TSVM-based estimator. predict(X)Performs classification on samples in X using the TSVM-based model. set_params(**params)Set the parameters of this estimator. -
fit(X, y)[source]¶ It fits the binary Least Squares TwinSVM model according to the given training data.
Parameters: X : array-like, shape (n_samples, n_features)
Training feature vectors, where n_samples is the number of samples and n_features is the number of features.
y : array-like, shape(n_samples,)
Target values or class labels.
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estimators.rbf_kernel(x, y, u)[source]¶ It transforms samples into higher dimension using Gaussian (RBF) kernel.
Parameters: x, y : array-like, shape (n_features,)
A feature vector or sample.
u : float
Parameter of the RBF kernel function.
Returns: float
Value of kernel matrix for feature vector x and y.