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- builtins.object
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- sega_learn.svm.baseSVM.BaseSVM
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- sega_learn.svm.generalizedSVM.GeneralizedSVC
- sega_learn.svm.generalizedSVM.GeneralizedSVR
- sega_learn.svm.linerarSVM.LinearSVC
- sega_learn.svm.linerarSVM.LinearSVR
- sega_learn.svm.oneClassSVM.OneClassSVM
class BaseSVM(builtins.object) |
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BaseSVM(C=1.0, tol=0.0001, max_iter=1000, learning_rate=0.01, kernel='linear', degree=3, gamma='scale', coef0=0.0, regression=False)
BaseSVM: A base class for Support Vector Machines (SVM) with kernel support.
This class provides the foundation for implementing SVM models with various kernels
and supports both classification and regression tasks.
Attributes:
C (float): Regularization parameter. Default is 1.0.
tol (float): Tolerance for stopping criteria. Default is 1e-4.
max_iter (int): Maximum number of iterations for optimization. Default is 1000.
learning_rate (float): Step size for optimization. Default is 0.01.
kernel (str): Kernel type ('linear', 'poly', 'rbf', or 'sigmoid'). Default is 'linear'.
degree (int): Degree for polynomial kernel. Default is 3.
gamma (str or float): Kernel coefficient ('scale', 'auto', or float). Default is 'scale'.
coef0 (float): Independent term in poly and sigmoid kernels. Default is 0.0.
regression (bool): Whether to use regression (SVR) or classification (SVC). Default is False.
w (ndarray): Weight vector for linear kernel.
b (float): Bias term.
support_vectors_ (ndarray): Support vectors identified during training.
support_vector_labels_ (ndarray): Labels of the support vectors.
support_vector_alphas_ (ndarray): Lagrange multipliers for the support vectors.
Methods:
__init__(self, C=1.0, tol=1e-4, max_iter=1000, learning_rate=0.01, kernel='linear', degree=3, gamma='scale', coef0=0.0, regression=False):
Initializes the BaseSVM instance with specified hyperparameters.
fit(self, X, y=None):
Fits the SVM model to the training data.
_fit(self, X, y):
Abstract method to be implemented by subclasses for training.
_compute_kernel(self, X1, X2):
Computes the kernel function between two input matrices.
decision_function(self, X):
Computes the decision function for input samples.
predict(self, X):
Predicts class labels for input samples.
score(self, X, y):
Computes the mean accuracy of the model on the given test data.
get_params(self, deep=True):
Retrieves the hyperparameters of the model.
set_params(self, **parameters):
Sets the hyperparameters of the model.
__sklearn_is_fitted__(self):
Checks if the model has been fitted (for sklearn compatibility).
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Methods defined here:
- __init__(self, C=1.0, tol=0.0001, max_iter=1000, learning_rate=0.01, kernel='linear', degree=3, gamma='scale', coef0=0.0, regression=False)
- Initializes the BaseSVM instance with specified hyperparameters.
Args:
C: (float) - Regularization parameter. Default is 1.0.
tol: (float) - Tolerance for stopping criteria. Default is 1e-4.
max_iter: (int) - Maximum number of iterations for optimization. Default is 1000.
learning_rate: (float) - Step size for optimization. Default is 0.01.
kernel: (str) - Kernel type ('linear', 'poly', 'rbf', or 'sigmoid'). Default is 'linear'.
degree: (int) - Degree for polynomial kernel. Default is 3.
gamma: (str or float) - Kernel coefficient ('scale', 'auto', or float). Default is 'scale'.
coef0: (float) - Independent term in poly and sigmoid kernels. Default is 0.0.
regression: (bool) - Whether to use regression (SVR) or classification (SVC). Default is False.
- __sklearn_is_fitted__(self)
- Checks if the model has been fitted (for sklearn compatibility).
Returns:
fitted: (bool) - True if the model has been fitted, otherwise False.
- decision_function(self, X)
- Computes the decision function for input samples.
Args:
X: (array-like of shape (n_samples, n_features)) - Input samples.
Returns:
decision_values: (ndarray of shape (n_samples,)) - Decision function values.
- fit(self, X, y=None)
- Fits the SVM model to the training data.
Args:
X: (array-like of shape (n_samples, n_features)) - Training vectors.
y: (array-like of shape (n_samples,)) - Target values. Default is None.
Returns:
self: (BaseSVM) - The fitted instance.
- get_params(self, deep=True)
- Retrieves the hyperparameters of the model.
Args:
deep: (bool) - If True, returns parameters of subobjects as well. Default is True.
Returns:
params: (dict) - Dictionary of hyperparameter names and values.
- predict(self, X)
- Predicts class labels for input samples.
Args:
X: (array-like of shape (n_samples, n_features)) - Input samples.
Returns:
predicted_labels: (ndarray of shape (n_samples,)) - Predicted class labels.
- score(self, X, y)
- Computes the mean accuracy of the model on the given test data.
Args:
X: (array-like of shape (n_samples, n_features)) - Test samples.
y: (array-like of shape (n_samples,)) - True class labels.
Returns:
score: (float) - Mean accuracy of predictions.
- set_params(self, **parameters)
- Sets the hyperparameters of the model.
Args:
**parameters: (dict) - Hyperparameter names and values.
Returns:
self: (BaseSVM) - The updated estimator instance.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class GeneralizedSVC(sega_learn.svm.baseSVM.BaseSVM) |
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GeneralizedSVC(C=1.0, tol=0.0001, max_iter=1000, learning_rate=0.01, kernel='linear', degree=3, gamma='scale', coef0=0.0)
GeneralizedSVC: A Support Vector Classifier (SVC) model with support for multiple kernels.
This class implements an SVC model using gradient descent for optimization. It supports
linear and non-linear kernels, including polynomial and RBF kernels.
Attributes:
C (float): Regularization parameter. Default is 1.0.
tol (float): Tolerance for stopping criteria. Default is 1e-4.
max_iter (int): Maximum number of iterations for gradient descent. Default is 1000.
learning_rate (float): Learning rate for gradient descent. Default is 0.01.
kernel (str): Kernel type ('linear', 'poly', 'rbf'). Default is 'linear'.
degree (int): Degree of the polynomial kernel function ('poly'). Ignored by other kernels. Default is 3.
gamma (str or float): Kernel coefficient for 'rbf' and 'poly'. Default is 'scale'.
coef0 (float): Independent term in kernel function ('poly'). Default is 0.0.
Methods:
__init__(self, C=1.0, tol=1e-4, max_iter=1000, learning_rate=0.01, kernel="linear", degree=3, gamma="scale", coef0=0.0):
Initialize the GeneralizedSVC model with specified hyperparameters.
_fit(self, X, y):
Fit the GeneralizedSVC model to the training data using gradient descent.
_predict_binary(self, X):
Predict binary class labels for input samples.
_predict_multiclass(self, X):
Predict multi-class labels using one-vs-rest strategy.
decision_function(self, X):
Compute raw decision function values for input samples.
_score_binary(self, X, y):
Compute the accuracy score for binary classification.
_score_multiclass(self, X, y):
Compute the accuracy score for multi-class classification.
Raises:
ValueError: If numerical instability is detected during training.
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- Method resolution order:
- GeneralizedSVC
- sega_learn.svm.baseSVM.BaseSVM
- builtins.object
Methods defined here:
- __init__(self, C=1.0, tol=0.0001, max_iter=1000, learning_rate=0.01, kernel='linear', degree=3, gamma='scale', coef0=0.0)
- Initializes the GeneralizedSVC model with specified hyperparameters.
Args:
C: (float) - Regularization parameter. Default is 1.0.
tol: (float) - Tolerance for stopping criteria. Default is 1e-4.
max_iter: (int) - Maximum number of iterations for gradient descent. Default is 1000.
learning_rate: (float) - Learning rate for gradient descent. Default is 0.01.
kernel: (str) - Kernel type ('linear', 'poly', 'rbf'). Default is 'linear'.
degree: (int) - Degree of the polynomial kernel function ('poly'). Ignored by other kernels. Default is 3.
gamma: (str or float) - Kernel coefficient for 'rbf' and 'poly'. Default is 'scale'.
coef0: (float) - Independent term in kernel function ('poly'). Default is 0.0.
- decision_function(self, X)
- Compute raw decision function values for input samples.
Methods inherited from sega_learn.svm.baseSVM.BaseSVM:
- __sklearn_is_fitted__(self)
- Checks if the model has been fitted (for sklearn compatibility).
Returns:
fitted: (bool) - True if the model has been fitted, otherwise False.
- fit(self, X, y=None)
- Fits the SVM model to the training data.
Args:
X: (array-like of shape (n_samples, n_features)) - Training vectors.
y: (array-like of shape (n_samples,)) - Target values. Default is None.
Returns:
self: (BaseSVM) - The fitted instance.
- get_params(self, deep=True)
- Retrieves the hyperparameters of the model.
Args:
deep: (bool) - If True, returns parameters of subobjects as well. Default is True.
Returns:
params: (dict) - Dictionary of hyperparameter names and values.
- predict(self, X)
- Predicts class labels for input samples.
Args:
X: (array-like of shape (n_samples, n_features)) - Input samples.
Returns:
predicted_labels: (ndarray of shape (n_samples,)) - Predicted class labels.
- score(self, X, y)
- Computes the mean accuracy of the model on the given test data.
Args:
X: (array-like of shape (n_samples, n_features)) - Test samples.
y: (array-like of shape (n_samples,)) - True class labels.
Returns:
score: (float) - Mean accuracy of predictions.
- set_params(self, **parameters)
- Sets the hyperparameters of the model.
Args:
**parameters: (dict) - Hyperparameter names and values.
Returns:
self: (BaseSVM) - The updated estimator instance.
Data descriptors inherited from sega_learn.svm.baseSVM.BaseSVM:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class GeneralizedSVR(sega_learn.svm.baseSVM.BaseSVM) |
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GeneralizedSVR(C=1.0, tol=0.0001, max_iter=1000, learning_rate=0.01, epsilon=0.1, kernel='linear', degree=3, gamma='scale', coef0=0.0)
GeneralizedSVR: A Support Vector Regression (SVR) model with support for multiple kernels.
This class implements an SVR model using gradient descent for optimization. It supports
linear and non-linear kernels, including polynomial and RBF kernels.
Attributes:
C (float): Regularization parameter. Default is 1.0.
tol (float): Tolerance for stopping criteria. Default is 1e-4.
max_iter (int): Maximum number of iterations for gradient descent. Default is 1000.
learning_rate (float): Learning rate for gradient descent. Default is 0.01.
epsilon (float): Epsilon parameter for epsilon-insensitive loss. Default is 0.1.
kernel (str): Kernel type ('linear', 'poly', 'rbf'). Default is 'linear'.
degree (int): Degree of the polynomial kernel function ('poly'). Ignored by other kernels. Default is 3.
gamma (str or float): Kernel coefficient for 'rbf' and 'poly'. Default is 'scale'.
coef0 (float): Independent term in kernel function ('poly'). Default is 0.0.
Methods:
__init__(self, C=1.0, tol=1e-4, max_iter=1000, learning_rate=0.01, epsilon=0.1, kernel="linear", degree=3, gamma="scale", coef0=0.0):
Initialize the GeneralizedSVR model with specified hyperparameters.
_fit(self, X, y):
Fit the GeneralizedSVR model to the training data using gradient descent.
predict(self, X):
Predict continuous target values for input samples.
decision_function(self, X):
Compute raw decision function values for input samples.
score(self, X, y):
Compute the coefficient of determination (R² score) for the model's predictions.
Raises:
ValueError: If numerical instability is detected during training.
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- Method resolution order:
- GeneralizedSVR
- sega_learn.svm.baseSVM.BaseSVM
- builtins.object
Methods defined here:
- __init__(self, C=1.0, tol=0.0001, max_iter=1000, learning_rate=0.01, epsilon=0.1, kernel='linear', degree=3, gamma='scale', coef0=0.0)
- Initializes the GeneralizedSVR model with specified hyperparameters.
Args:
C: (float) - Regularization parameter. Default is 1.0.
tol: (float) - Tolerance for stopping criteria. Default is 1e-4.
max_iter: (int) - Maximum number of iterations for gradient descent. Default is 1000.
learning_rate: (float) - Learning rate for gradient descent. Default is 0.01.
epsilon: (float) - Epsilon parameter for epsilon-insensitive loss. Default is 0.1.
kernel: (str) - Kernel type ('linear', 'poly', 'rbf'). Default is 'linear'.
degree: (int) - Degree of the polynomial kernel function ('poly'). Ignored by other kernels. Default is 3.
gamma: (str or float) - Kernel coefficient for 'rbf' and 'poly'. Default is 'scale'.
coef0: (float) - Independent term in kernel function ('poly'). Default is 0.0.
- decision_function(self, X)
- Compute raw decision function values for input samples.
- predict(self, X)
- Predict continuous target values for input samples.
- score(self, X, y)
- Compute the coefficient of determination (R² score) for the model's predictions.
Methods inherited from sega_learn.svm.baseSVM.BaseSVM:
- __sklearn_is_fitted__(self)
- Checks if the model has been fitted (for sklearn compatibility).
Returns:
fitted: (bool) - True if the model has been fitted, otherwise False.
- fit(self, X, y=None)
- Fits the SVM model to the training data.
Args:
X: (array-like of shape (n_samples, n_features)) - Training vectors.
y: (array-like of shape (n_samples,)) - Target values. Default is None.
Returns:
self: (BaseSVM) - The fitted instance.
- get_params(self, deep=True)
- Retrieves the hyperparameters of the model.
Args:
deep: (bool) - If True, returns parameters of subobjects as well. Default is True.
Returns:
params: (dict) - Dictionary of hyperparameter names and values.
- set_params(self, **parameters)
- Sets the hyperparameters of the model.
Args:
**parameters: (dict) - Hyperparameter names and values.
Returns:
self: (BaseSVM) - The updated estimator instance.
Data descriptors inherited from sega_learn.svm.baseSVM.BaseSVM:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class LinearSVC(sega_learn.svm.baseSVM.BaseSVM) |
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LinearSVC(C=1.0, tol=0.0001, max_iter=1000, learning_rate=0.01, numba=False)
LinearSVC is a linear Support Vector Classifier (SVC) implementation that uses gradient descent for optimization.
It supports binary and multi-class classification using a one-vs-rest strategy.
Attributes:
C (float): Regularization parameter. Default is 1.0.
tol (float): Tolerance for stopping criteria. Default is 1e-4.
max_iter (int): Maximum number of iterations for gradient descent. Default is 1000.
learning_rate (float): Learning rate for gradient descent. Default is 0.01.
numba (bool): Whether to use Numba-accelerated computations. Default is False.
w (ndarray): Weight vector for the linear model.
b (float): Bias term for the linear model.
numba_available (bool): Indicates if Numba is available for use.
Methods:
__init__(self, C=1.0, tol=1e-4, max_iter=1000, learning_rate=0.01, numba=False):
Initializes the LinearSVC instance with hyperparameters and checks for Numba availability.
_fit(self, X, y):
Fits the LinearSVC model to the training data using gradient descent.
_predict_binary(self, X):
Predicts class labels {-1, 1} for binary classification.
_predict_multiclass(self, X):
Predicts class labels for multi-class classification using one-vs-rest strategy.
decision_function(self, X):
Computes raw decision function values before thresholding.
_score_binary(self, X, y):
Computes the mean accuracy of predictions for binary classification.
_score_multiclass(self, X, y):
Computes the mean accuracy of predictions for multi-class classification.
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- Method resolution order:
- LinearSVC
- sega_learn.svm.baseSVM.BaseSVM
- builtins.object
Methods defined here:
- __init__(self, C=1.0, tol=0.0001, max_iter=1000, learning_rate=0.01, numba=False)
- Initializes the LinearSVC instance with hyperparameters and checks for Numba availability.
Args:
C: (float) - Regularization parameter. Default is 1.0.
tol: (float) - Tolerance for stopping criteria. Default is 1e-4.
max_iter: (int) - Maximum number of iterations for gradient descent. Default is 1000.
learning_rate: (float) - Learning rate for gradient descent. Default is 0.01.
numba: (bool) - Whether to use Numba-accelerated computations. Default is False.
- decision_function(self, X)
- Compute raw decision function values before thresholding.
Args:
X (array-like of shape (n_samples, n_features)): Input samples.
Returns:
scores (array of shape (n_samples,)): Decision function values.
Methods inherited from sega_learn.svm.baseSVM.BaseSVM:
- __sklearn_is_fitted__(self)
- Checks if the model has been fitted (for sklearn compatibility).
Returns:
fitted: (bool) - True if the model has been fitted, otherwise False.
- fit(self, X, y=None)
- Fits the SVM model to the training data.
Args:
X: (array-like of shape (n_samples, n_features)) - Training vectors.
y: (array-like of shape (n_samples,)) - Target values. Default is None.
Returns:
self: (BaseSVM) - The fitted instance.
- get_params(self, deep=True)
- Retrieves the hyperparameters of the model.
Args:
deep: (bool) - If True, returns parameters of subobjects as well. Default is True.
Returns:
params: (dict) - Dictionary of hyperparameter names and values.
- predict(self, X)
- Predicts class labels for input samples.
Args:
X: (array-like of shape (n_samples, n_features)) - Input samples.
Returns:
predicted_labels: (ndarray of shape (n_samples,)) - Predicted class labels.
- score(self, X, y)
- Computes the mean accuracy of the model on the given test data.
Args:
X: (array-like of shape (n_samples, n_features)) - Test samples.
y: (array-like of shape (n_samples,)) - True class labels.
Returns:
score: (float) - Mean accuracy of predictions.
- set_params(self, **parameters)
- Sets the hyperparameters of the model.
Args:
**parameters: (dict) - Hyperparameter names and values.
Returns:
self: (BaseSVM) - The updated estimator instance.
Data descriptors inherited from sega_learn.svm.baseSVM.BaseSVM:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class LinearSVR(sega_learn.svm.baseSVM.BaseSVM) |
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LinearSVR(C=1.0, tol=0.0001, max_iter=1000, learning_rate=0.01, epsilon=0.1, numba=False)
LinearSVR: A linear Support Vector Regression (SVR) model using epsilon-insensitive loss.
This class implements a linear SVR model with support for mini-batch gradient descent
and optional acceleration using Numba. It is designed for regression tasks and uses
epsilon-insensitive loss to handle errors within a specified margin.
Attributes:
C (float): Regularization parameter. Default is 1.0.
tol (float): Tolerance for stopping criteria. Default is 1e-4.
max_iter (int): Maximum number of iterations for gradient descent. Default is 1000.
learning_rate (float): Learning rate for gradient descent. Default is 0.01.
epsilon (float): Epsilon parameter for epsilon-insensitive loss. Default is 0.1.
numba (bool): Whether to use Numba for acceleration. Default is False.
w (ndarray): Weight vector of the model.
b (float): Bias term of the model.
numba_available (bool): Indicates if Numba is available for acceleration.
X_train (ndarray): Training data used for fitting.
y_train (ndarray): Target values used for fitting.
Methods:
__init__(self, C=1.0, tol=1e-4, max_iter=1000, learning_rate=0.01, epsilon=0.1, numba=False):
Initialize the LinearSVR model with specified hyperparameters.
_fit(self, X, y):
Fit the LinearSVR model to the training data using mini-batch gradient descent.
predict(self, X):
Predict continuous target values for input samples.
decision_function(self, X):
Compute raw decision function values for input samples.
score(self, X, y):
Compute the coefficient of determination (R² score) for the model's predictions.
Raises:
ValueError: If a non-linear kernel is specified, as LinearSVR only supports linear kernels.
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- Method resolution order:
- LinearSVR
- sega_learn.svm.baseSVM.BaseSVM
- builtins.object
Methods defined here:
- __init__(self, C=1.0, tol=0.0001, max_iter=1000, learning_rate=0.01, epsilon=0.1, numba=False)
- Initializes the LinearSVR instance with hyperparameters and checks for Numba availability.
Args:
C: (float) - Regularization parameter. Default is 1.0.
tol: (float) - Tolerance for stopping criteria. Default is 1e-4.
max_iter: (int) - Maximum number of iterations for gradient descent. Default is 1000.
learning_rate: (float) - Learning rate for gradient descent. Default is 0.01.
epsilon: (float) - Epsilon parameter for epsilon-insensitive loss. Default is 0.1.
numba: (bool) - Whether to use Numba-accelerated computations. Default is False.
Returns:
None
- decision_function(self, X)
- Compute raw decision function values.
Args:
X: (array-like of shape (n_samples, n_features)) - Input samples.
Returns:
scores: (array of shape (n_samples,)) - Predicted values.
- predict(self, X)
- Predict continuous target values for input samples.
Args:
X: (array-like of shape (n_samples, n_features)) - Input samples.
Returns:
y_pred: (array of shape (n_samples,)) - Predicted values.
- score(self, X, y)
- Compute the coefficient of determination (R² score).
Args:
X: (array-like of shape (n_samples, n_features)) - Test samples.
y: (array-like of shape (n_samples,)) - True target values.
Returns:
score: (float) - R² score of predictions.
Methods inherited from sega_learn.svm.baseSVM.BaseSVM:
- __sklearn_is_fitted__(self)
- Checks if the model has been fitted (for sklearn compatibility).
Returns:
fitted: (bool) - True if the model has been fitted, otherwise False.
- fit(self, X, y=None)
- Fits the SVM model to the training data.
Args:
X: (array-like of shape (n_samples, n_features)) - Training vectors.
y: (array-like of shape (n_samples,)) - Target values. Default is None.
Returns:
self: (BaseSVM) - The fitted instance.
- get_params(self, deep=True)
- Retrieves the hyperparameters of the model.
Args:
deep: (bool) - If True, returns parameters of subobjects as well. Default is True.
Returns:
params: (dict) - Dictionary of hyperparameter names and values.
- set_params(self, **parameters)
- Sets the hyperparameters of the model.
Args:
**parameters: (dict) - Hyperparameter names and values.
Returns:
self: (BaseSVM) - The updated estimator instance.
Data descriptors inherited from sega_learn.svm.baseSVM.BaseSVM:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class OneClassSVM(sega_learn.svm.baseSVM.BaseSVM) |
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OneClassSVM(C=1.0, tol=0.0001, max_iter=1000, learning_rate=0.01, kernel='linear', degree=3, gamma='scale', coef0=0.0)
OneClassSVM is a custom implementation of a One-Class Support Vector Machine (SVM) for anomaly detection using gradient descent.
It inherits from the BaseSVM class and supports various kernel functions.
Attributes:
support_vectors_ (array-like of shape (n_support_vectors, n_features)):
The support vectors identified during training.
support_vector_alphas_ (array-like of shape (n_support_vectors,)):
The Lagrange multipliers (alpha) corresponding to the support vectors.
b (float):
The bias term (rho) computed during training.
Methods:
__init__(C=1.0, tol=1e-4, max_iter=1000, learning_rate=0.01, kernel="linear",
degree=3, gamma="scale", coef0=0.0):
Initialize the OneClassSVM with hyperparameters.
_fit(X, y=None):
Fit the OneClassSVM model using gradient descent for anomaly detection.
decision_function(X):
Compute the decision function values for the input samples.
predict(X):
Predict whether the input samples are inliers (1) or outliers (-1).
score(X, y):
Compute the mean accuracy of predictions compared to true labels.
__sklearn_is_fitted__():
Check if the model has been fitted. For compatibility with sklearn.
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| |
- Method resolution order:
- OneClassSVM
- sega_learn.svm.baseSVM.BaseSVM
- builtins.object
Methods defined here:
- __init__(self, C=1.0, tol=0.0001, max_iter=1000, learning_rate=0.01, kernel='linear', degree=3, gamma='scale', coef0=0.0)
- Initialize the OneClassSVM with hyperparameters.
Args:
C: (float) - Regularization parameter (default is 1.0).
tol: (float) - Tolerance for stopping criteria (default is 1e-4).
max_iter: (int) - Maximum number of iterations (default is 1000).
learning_rate: (float) - Learning rate for gradient descent (default is 0.01).
kernel: (str) - Kernel type ("linear", "poly", "rbf", "sigmoid") (default is "linear").
degree: (int) - Degree for polynomial kernel (default is 3).
gamma: (str or float) - Kernel coefficient ("scale", "auto", or float) (default is "scale").
coef0: (float) - Independent term in kernel function (default is 0.0).
- __sklearn_is_fitted__(self)
- Check if the model has been fitted. For compatibility with sklearn.
Returns:
fitted: (bool) - True if the model has been fitted, otherwise False.
- decision_function(self, X)
- Compute the decision function values for the input samples.
- predict(self, X)
- Predict whether the input samples are inliers (1) or outliers (-1).
- score(self, X, y)
- Compute the mean accuracy of predictions.
Args:
X: (array-like of shape (n_samples, n_features)) - Test samples.
y: (array-like of shape (n_samples,)) - True labels (+1 for inliers, -1 for outliers).
Returns:
score: (float) - Mean accuracy of predictions.
Methods inherited from sega_learn.svm.baseSVM.BaseSVM:
- fit(self, X, y=None)
- Fits the SVM model to the training data.
Args:
X: (array-like of shape (n_samples, n_features)) - Training vectors.
y: (array-like of shape (n_samples,)) - Target values. Default is None.
Returns:
self: (BaseSVM) - The fitted instance.
- get_params(self, deep=True)
- Retrieves the hyperparameters of the model.
Args:
deep: (bool) - If True, returns parameters of subobjects as well. Default is True.
Returns:
params: (dict) - Dictionary of hyperparameter names and values.
- set_params(self, **parameters)
- Sets the hyperparameters of the model.
Args:
**parameters: (dict) - Hyperparameter names and values.
Returns:
self: (BaseSVM) - The updated estimator instance.
Data descriptors inherited from sega_learn.svm.baseSVM.BaseSVM:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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