# 'LinearRegression' object has no attribute 'positive'

Hi,

I have got this error while running the code in the lecture, any advise?

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 `

t`and`

p` 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