Support vector machines implementation using simplified SMO optimization. Parameters ---------- C : float, default 1.0 kernel : Kernel object tol : float , default 1e-3 max_iter : int, default 100
(self, C=1.0, kernel=None, tol=1e-3, max_iter=100)
| 16 | |
| 17 | class SVM(BaseEstimator): |
| 18 | def __init__(self, C=1.0, kernel=None, tol=1e-3, max_iter=100): |
| 19 | """Support vector machines implementation using simplified SMO optimization. |
| 20 | |
| 21 | Parameters |
| 22 | ---------- |
| 23 | C : float, default 1.0 |
| 24 | kernel : Kernel object |
| 25 | tol : float , default 1e-3 |
| 26 | max_iter : int, default 100 |
| 27 | """ |
| 28 | self.C = C |
| 29 | self.tol = tol |
| 30 | self.max_iter = max_iter |
| 31 | if kernel is None: |
| 32 | self.kernel = Linear() |
| 33 | else: |
| 34 | self.kernel = kernel |
| 35 | |
| 36 | self.b = 0 |
| 37 | self.alpha = None |
| 38 | self.K = None |
| 39 | |
| 40 | def fit(self, X, y=None): |
| 41 | self._setup_input(X, y) |