# Features in sklearn logistic regression

I have some problem with adding own features to sklearn.linear_model.LogisticRegression.
But anyway lets see some example code:

from sklearn.linear_model import LogisticRegression, LinearRegression
import numpy as np

# Numbers are class of tag
resultsNER = np.array([1,2,3,4,5])

# Acording to resultNER every row is another class so is another features
# but in this way every row have the same features
xNER = np.array([[1.,0.,0.,0.,-1.,1.],
[1.,0.,1.,0., 0.,1.],
[1.,1.,1.,1., 1.,1.],
[0.,0.,0.,0., 0.,0.],
[1.,1.,1.,0., 0.,0.]])

# Assing resultsNER to y
y = resultsNER
# Create LogReg
logit = LogisticRegression(C = 1.0)
# Learn LogReg
logit.fit(xNER, y)

# Some test vector to check wich class will be predict
xPP = np.array([1.,1.,1.,0.,0.,1.])

# linear = LinearRegression()
# linear.fit(x, y)

print "expected: ", y
print "predicted:", logit.predict(xPP)
print "decision: ", logit.decision_function(xNER)
print logit.coef_
# print linear.predict(x)
print "params: ", logit.get_params(deep=True)


Code above is clear and easy. So I have some classes which I called 1,2,3,4,5(resultsNER) they are according to some classes like data, person, organization etc. So for each class I make own features which return true or false in this case 1 and 0.

Example:
If token equals "(S|s)unday" is data class. In mathematical way is clear. I have token for each class features I test it. Then I look which class have the max value of sum of features (that’s why return number not boolean) and pick it up.
In other words: I use argmax function.
Of course in summary each feature has alpha coefficients. In this case is multi class classification so I need know how add multi class features to sklearn.LogisticRegression.

I need two things alpha coefficients and how add my own features to Logistic Regression. The most important for me is how add to sklearn.LogisticRegression my own features functions for each class. I know I can compute coefficients by gradient descent. But I think when I use fit(x,y) the LogisticRegression use some algorithm to compute coefficients which I can get by attribute .coef_ .

So in the end my main question is:

How to add own features for different classes in my example class 1,2,3,4,5(resultNER)?