def fit(self, X, y=None, prefit=False):
assert (y is None) or self.rows, 'If over features, cant use y'
if not self.rows:
X = X.T
self.n = X.shape[0]
self.D = np.sign(np.random.randn(self.n))
self.srht_const = np.sqrt(self.n / self.k)
self.S = np.random.choice(self.n, self.k, replace=False)
if prefit:
if len(X.shape) == 1:
self.transform_1d(X)
else:
self.transform(X, y)
export_wisdom(self.wisdom_file)
def __del__(self):
export_wisdom(self.wisdom_file)
