def predict(self, X=None, K0_whole_test=None, K1_whole_test=None, iid_if_none=None, count_A1=None):
    '\n        Method for predicting from a fitted :class:`FastLMM` predictor.\n        If the examples in X, K0_whole_test, K1_whole_test are not the same, they will be reordered and intersected.\n\n        :param X: testing covariate information, optional: \n          If you give a string, it should be the file name of a PLINK phenotype-formatted file.\n        :type X: a PySnpTools `SnpReader <http://fastlmm.github.io/PySnpTools/#snpreader-snpreader>`__ (such as `Pheno <http://fastlmm.github.io/PySnpTools/#snpreader-pheno>`__ or `SnpData <http://fastlmm.github.io/PySnpTools/#snpreader-snpdata>`__) or string.\n\n        :param K0_whole_test: Must be None. Represents the identity similarity matrix.\n        :type K0_whole_test: None\n\n        :param K1_whole_test: Must be None. Represents the identity similarity matrix.\n        :type K1_whole_test: `SnpReader <http://fastlmm.github.io/PySnpTools/#snpreader-snpreader>`__ or a string or `KernelReader <http://fastlmm.github.io/PySnpTools/#kernelreader-kernelreader>`__\n\n        :param iid_if_none: Examples to predict for if no X, K0_whole_test, K1_whole_test is provided.\n        :type iid_if_none: an ndarray of two strings\n\n        :param count_A1: If it needs to read SNP data from a BED-formatted file, tells if it should count the number of A1\n             alleles (the PLINK standard) or the number of A2 alleles. False is the current default, but in the future the default will change to True.\n        :type count_A1: bool\n\n        :rtype: A `SnpData <http://fastlmm.github.io/PySnpTools/#snpreader-snpdata>`__ of the means and a :class:`KernelData` of the covariance\n        '
    assert self.is_fitted, 'Can only predict after predictor has been fitted'
    assert ((K0_whole_test is None) or isinstance(K0_whole_test, KernelIdentity))
    assert ((K1_whole_test is None) or isinstance(K1_whole_test, KernelIdentity))
    X = _pheno_fixup(X, iid_if_none=iid_if_none, count_A1=count_A1)
    X = X.read().standardize(self.covar_unit_trained)
    X = SnpData(iid=X.iid, sid=FastLMM._new_snp_name(X), val=np.c_[(X.read().val, np.ones((X.iid_count, 1)))])
    assert np.array_equal(X.sid, self.covar_sid), 'Expect covar sids to be the same in train and test.'
    pheno_predicted = X.val.dot(self.beta).reshape((- 1), 1)
    ret0 = SnpData(iid=X.iid, sid=self.pheno_sid, val=pheno_predicted, pos=np.array([[np.nan, np.nan, np.nan]]), name='linear regression Prediction')
    from pysnptools.kernelreader import KernelData
    ret1 = KernelData(iid=X.iid, val=((np.eye(X.iid_count) * self.ssres) / self.iid_count))
    return (ret0, ret1)
Method for predicting from a fitted :class:`FastLMM` predictor.
If the examples in X, K0_whole_test, K1_whole_test are not the same, they will be reordered and intersected.

:param X: testing covariate information, optional: 
  If you give a string, it should be the file name of a PLINK phenotype-formatted file.
:type X: a PySnpTools `SnpReader <http://fastlmm.github.io/PySnpTools/#snpreader-snpreader>`__ (such as `Pheno <http://fastlmm.github.io/PySnpTools/#snpreader-pheno>`__ or `SnpData <http://fastlmm.github.io/PySnpTools/#snpreader-snpdata>`__) or string.

:param K0_whole_test: Must be None. Represents the identity similarity matrix.
:type K0_whole_test: None

:param K1_whole_test: Must be None. Represents the identity similarity matrix.
:type K1_whole_test: `SnpReader <http://fastlmm.github.io/PySnpTools/#snpreader-snpreader>`__ or a string or `KernelReader <http://fastlmm.github.io/PySnpTools/#kernelreader-kernelreader>`__

:param iid_if_none: Examples to predict for if no X, K0_whole_test, K1_whole_test is provided.
:type iid_if_none: an ndarray of two strings

:param count_A1: If it needs to read SNP data from a BED-formatted file, tells if it should count the number of A1
     alleles (the PLINK standard) or the number of A2 alleles. False is the current default, but in the future the default will change to True.
:type count_A1: bool

:rtype: A `SnpData <http://fastlmm.github.io/PySnpTools/#snpreader-snpdata>`__ of the means and a :class:`KernelData` of the covariance
def predict(self, X=None, K0_whole_test=None, K1_whole_test=None, iid_if_none=None, count_A1=None):
    '\n        Method for predicting from a fitted :class:`FastLMM` predictor.\n        If the examples in X, K0_whole_test, K1_whole_test are not the same, they will be reordered and intersected.\n\n        :param X: testing covariate information, optional: \n          If you give a string, it should be the file name of a PLINK phenotype-formatted file.\n        :type X: a PySnpTools `SnpReader <http://fastlmm.github.io/PySnpTools/#snpreader-snpreader>`__ (such as `Pheno <http://fastlmm.github.io/PySnpTools/#snpreader-pheno>`__ or `SnpData <http://fastlmm.github.io/PySnpTools/#snpreader-snpdata>`__) or string.\n\n        :param K0_whole_test: Must be None. Represents the identity similarity matrix.\n        :type K0_whole_test: None\n\n        :param K1_whole_test: Must be None. Represents the identity similarity matrix.\n        :type K1_whole_test: `SnpReader <http://fastlmm.github.io/PySnpTools/#snpreader-snpreader>`__ or a string or `KernelReader <http://fastlmm.github.io/PySnpTools/#kernelreader-kernelreader>`__\n\n        :param iid_if_none: Examples to predict for if no X, K0_whole_test, K1_whole_test is provided.\n        :type iid_if_none: an ndarray of two strings\n\n        :param count_A1: If it needs to read SNP data from a BED-formatted file, tells if it should count the number of A1\n             alleles (the PLINK standard) or the number of A2 alleles. False is the current default, but in the future the default will change to True.\n        :type count_A1: bool\n\n        :rtype: A `SnpData <http://fastlmm.github.io/PySnpTools/#snpreader-snpdata>`__ of the means and a :class:`KernelData` of the covariance\n        '
    assert self.is_fitted, 'Can only predict after predictor has been fitted'
    assert ((K0_whole_test is None) or isinstance(K0_whole_test, KernelIdentity))
    assert ((K1_whole_test is None) or isinstance(K1_whole_test, KernelIdentity))
    X = _pheno_fixup(X, iid_if_none=iid_if_none, count_A1=count_A1)
    X = X.read().standardize(self.covar_unit_trained)
    X = SnpData(iid=X.iid, sid=FastLMM._new_snp_name(X), val=np.c_[(X.read().val, np.ones((X.iid_count, 1)))])
    assert np.array_equal(X.sid, self.covar_sid), 'Expect covar sids to be the same in train and test.'
    pheno_predicted = X.val.dot(self.beta).reshape((- 1), 1)
    ret0 = SnpData(iid=X.iid, sid=self.pheno_sid, val=pheno_predicted, pos=np.array([[np.nan, np.nan, np.nan]]), name='linear regression Prediction')
    from pysnptools.kernelreader import KernelData
    ret1 = KernelData(iid=X.iid, val=((np.eye(X.iid_count) * self.ssres) / self.iid_count))
    return (ret0, ret1)