def fit(self, x, y, history): self._svm = IncrementalMultiSVM( dtype=x.dtype, n_features=x.shape[1], n_classes=2, l2_regularization=self.l2_regularization, n_sgd_iters=0, bfgs_kwargs={ 'maxfun': 1000, 'iprint': 0, 'm': 32, 'factr': 100 }, ) self._svm.fit(x, (y + 1) / 2, history)
class IncrementalSVM_MultiHack(object): def __init__(self, l2_regularization): self.l2_regularization = l2_regularization def fit(self, x, y, history): self._svm = IncrementalMultiSVM( dtype=x.dtype, n_features=x.shape[1], n_classes=2, l2_regularization=self.l2_regularization, n_sgd_iters=0, bfgs_kwargs={ 'maxfun': 1000, 'iprint': 0, 'm': 32, 'factr': 100}, ) self._svm.fit(x, (y + 1) / 2, history) def predict(self, x, history): return self._svm.predict(x, history) * 2 - 1
class IncrementalSVM_MultiHack(object): def __init__(self, l2_regularization): self.l2_regularization = l2_regularization def fit(self, x, y, history): self._svm = IncrementalMultiSVM( dtype=x.dtype, n_features=x.shape[1], n_classes=2, l2_regularization=self.l2_regularization, n_sgd_iters=0, bfgs_kwargs={ 'maxfun': 1000, 'iprint': 0, 'm': 32, 'factr': 100 }, ) self._svm.fit(x, (y + 1) / 2, history) def predict(self, x, history): return self._svm.predict(x, history) * 2 - 1
def fit(self, x, y, history): self._svm = IncrementalMultiSVM( dtype=x.dtype, n_features=x.shape[1], n_classes=2, l2_regularization=self.l2_regularization, n_sgd_iters=0, bfgs_kwargs={ 'maxfun': 1000, 'iprint': 0, 'm': 32, 'factr': 100}, ) self._svm.fit(x, (y + 1) / 2, history)