<commit_before>from __future__ import print_function
import functools
import numpy as np
from scipy.stats import norm as ndist

import regreg.api as rr

from .base import restricted_estimator
from ..constraints.affine import constraints
from .query import affine_gaussian_sampler
from .randomization import randomization
from ..algorithms.debiased_lasso import debiasing_matrix

class topK(object):

    def __init__(self,
                 observed_data,
                 covariance, 
                 randomizer_scale,
                 K,
                 abs=False, # absolute value of scores or not?
                 perturb=None):

        self.observed_score_state = -observed_data  # -Z if Z \sim N(\mu,\Sigma), X^Ty in regression setting
        self.nfeature = p = self.observed_score_state.shape[0]
        self.covariance = covariance
        randomized_stdev = np.sqrt(np.diag(covariance) + randomizer_scale**2)
        self.randomizer = randomization.isotropic_gaussian((p,), randomizer_scale)
        self._initial_omega = perturb
        self.K = K
        self._abs = abs

    def fit(self, perturb=None):

        p = self.nfeature

        # take a new perturbation if supplied<commit_msg>topK inherits from marginal_screening to get the targets<commit_after>from __future__ import print_function
import functools
import numpy as np
from scipy.stats import norm as ndist

import regreg.api as rr

from .base import restricted_estimator
from ..constraints.affine import constraints
from .query import affine_gaussian_sampler
from .randomization import randomization
from .marginal_screeing import marginal_screening
from ..algorithms.debiased_lasso import debiasing_matrix

class topK(marginal_screening):

    def __init__(self,
                 observed_data,
                 covariance, 
                 randomizer_scale,
                 K,
                 abs=False, # absolute value of scores or not?
                 perturb=None):

        self.observed_score_state = -observed_data  # -Z if Z \sim N(\mu,\Sigma), X^Ty in regression setting
        self.nfeature = p = self.observed_score_state.shape[0]
        self.covariance = covariance
        randomized_stdev = np.sqrt(np.diag(covariance) + randomizer_scale**2)
        self.randomizer = randomization.isotropic_gaussian((p,), randomizer_scale)
        self._initial_omega = perturb
        self.K = K
        self._abs = abs

    def fit(self, perturb=None):

        p = self.nfeature

        # take a new perturbation if supplied
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{'dir': 'git-commits-cleaned', 'id': '4094563', 'n_tokens_mistral': 729, 'n_tokens_neox': 686, 'n_words': 200}