kernel_approximation.py 文件源码

def _transform_dense(self, X):
        non_zero = (X != 0.0)
        X_nz = X[non_zero]

        X_step = np.zeros_like(X)
        X_step[non_zero] = np.sqrt(X_nz * self.sample_interval_)

        X_new = [X_step]

        log_step_nz = self.sample_interval_ * np.log(X_nz)
        step_nz = 2 * X_nz * self.sample_interval_

        for j in range(1, self.sample_steps):
            factor_nz = np.sqrt(step_nz /
                                np.cosh(np.pi * j * self.sample_interval_))

            X_step = np.zeros_like(X)
            X_step[non_zero] = factor_nz * np.cos(j * log_step_nz)
            X_new.append(X_step)

            X_step = np.zeros_like(X)
            X_step[non_zero] = factor_nz * np.sin(j * log_step_nz)
            X_new.append(X_step)

        return np.hstack(X_new)