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- builtins.object
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- BaseSVM
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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