Table of Contents

Mojo struct

SVC

@memory_only
struct SVC

Support Vector Classification.

Aliases

  • __del__is_trivial = False

Fields

  • C (Float64): Regularization parameter. When C != 0, C-Support Vector Classification model will be used.
  • nu (Float64): An upper bound on the fraction of margin errors and a lower bound of the fraction of support vectors. When nu != 0, Nu-Support Vector Classification model will be used.
  • kernel (String): Specifies the kernel type to be used in the algorithm: {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'}.
  • degree (Int): Degree of the polynomial kernel function ('poly').
  • gamma (Float64): Kernel coefficient for 'rbf', 'poly' and 'sigmoid': if gamma = -1 (default) is passed then it uses 1 / (n_features * X.var()); if gamma = -0.1, it uses 1 / n_features; if custom value, it must be non-negative.
  • coef0 (Float64): Independent term in kernel function. It is only significant in 'poly' and 'sigmoid'.
  • cache_size (Float64): Specify the size of the kernel cache (in MB).
  • tol (Float64): Tolerance for stopping criterion.
  • shrinking (Bool): Whether to use the shrinking heuristic.
  • probability (Bool): Whether to enable probability estimates.

Implemented traits

AnyType, CV, ImplicitlyDestructible

Methods

__init__

fn __init__(out self, C: Float64 = 0, nu: Float64 = 0, kernel: String = "rbf", degree: Int = 2, gamma: Float64 = -1, coef0: Float64 = 0, cache_size: Float64 = 200, tol: Float64 = 0.001, shrinking: Bool = True, probability: Bool = False, random_state: Int = -1)

Args:

  • C (Float64)
  • nu (Float64)
  • kernel (String)
  • degree (Int)
  • gamma (Float64)
  • coef0 (Float64)
  • cache_size (Float64)
  • tol (Float64)
  • shrinking (Bool)
  • probability (Bool)
  • random_state (Int)
  • self (Self)

Returns:

Self

fn __init__(out self, params: Dict[String, String])

Args:

  • params (Dict)
  • self (Self)

Returns:

Self

Raises:

__del__

fn __del__(deinit self)

Args:

  • self (Self)

fit

fn fit(mut self, X: Matrix, y: Matrix)

Fit the SVM model according to the given training data.

Args:

  • self (Self)
  • X (Matrix)
  • y (Matrix)

Raises:

predict

fn predict(self, X: Matrix) -> Matrix

Perform classification on samples in X.

Args:

  • self (Self)
  • X (Matrix)

Returns:

Matrix: The predicted classes.

Raises:

decision_function

fn decision_function(self, X: Matrix) -> List[List[Float64]]

Evaluate the decision function for the samples in X.

Args:

  • self (Self)
  • X (Matrix)

Returns:

List: The decision values in a 2D List format.

support_vectors

fn support_vectors(self) -> Matrix

Get support vectors.

Args:

  • self (Self)

Returns:

Matrix

Raises:

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