'LinearRegression' object has no attribute 'positive'

I have faced the same issue, but in my case it was when I created a derived class from LinearRegression that contained some extra functionality to calculate the p-value.
The missing part is initializing the base class during the initialization of the derived class, as I suspect the attribute "positive" has been added in a newer version.

I have added:
super().__init__(fit_intercept=fit_intercept, normalize=normalize, copy_X=copy_X, n_jobs=n_jobs)

Here full code:

class LinearRegression(linear_model.LinearRegression):
    """
    LinearRegression class after sklearn's, but calculate t-statistics
    and p-values for model coefficients (betas).
    Additional attributes available after .fit()
    are tandp which are of the shape (y.shape[1], X.shape[1])
    which is (n_features, n_coefs)
    This class sets the intercept to 0 by default, since usually we include it
    in X.
    """

    # nothing changes in __init__
    def __init__(self, fit_intercept=True, normalize=False, copy_X=True,
                 n_jobs=1):
        super().__init__(fit_intercept=fit_intercept, normalize=normalize, copy_X=copy_X, n_jobs=n_jobs)
        self.fit_intercept = fit_intercept
        self.normalize = normalize
        self.copy_X = copy_X
        self.n_jobs = n_jobs

Following Fernando's answer, I solved the issue, adding 'positive' attribute:

super().__init__(fit_intercept=fit_intercept, normalize=normalize, copy_X=copy_X, n_jobs=n_jobs, positive=False)
self.positive = positive

'positive' = True, forces coefficient to be positive.

I am getting the same error while running the code

AttributeError: 'LinearRegression' object has no attribute 'positive'

Please also what is this positive attribute all about.
I also want to understand the concept behind the p-value class method.
What is the linear Algebra behind the logistic regression function?

You need to update the code from Fernando a bit to make it work (at least on my end):

# nothing changes in __init__
    def __init__(self, fit_intercept=True, normalize=False, copy_X=True,
                 n_jobs=1):
        super().__init__(fit_intercept=fit_intercept, copy_X=copy_X, n_jobs=n_jobs)
        self.fit_intercept = fit_intercept
        self.normalize = normalize
        self.copy_X = copy_X
        self.n_jobs = n_jobs