
from sklearn.linear_model import RidgeClassifier
import numpy as np


class PRidgeClassifier(RidgeClassifier):

    def predict_proba(self, X):
#
        d = self.decision_function(X)
        prob = np.exp(d)/(1+np.exp(d))
        pdv = np.array([1-prob, prob])
        pdv = pdv.T
        return pdv

d78dc6ef2d0787ef9bbaa8e841bf944ef258057d
<mask token>

<mask token>


class PRidgeClassifier(RidgeClassifier):
    <mask token>

<mask token>


class PRidgeClassifier(RidgeClassifier):

    def predict_proba(self, X):
        d = self.decision_function(X)
        prob = np.exp(d) / (1 + np.exp(d))
        pdv = np.array([1 - prob, prob])
        pdv = pdv.T
        return pdv

from sklearn.linear_model import RidgeClassifier
import numpy as np


class PRidgeClassifier(RidgeClassifier):

    def predict_proba(self, X):
        d = self.decision_function(X)
        prob = np.exp(d) / (1 + np.exp(d))
        pdv = np.array([1 - prob, prob])
        pdv = pdv.T
        return pdv


from sklearn.linear_model import RidgeClassifier
import numpy as np


class PRidgeClassifier(RidgeClassifier):

    def predict_proba(self, X):
#
        d = self.decision_function(X)
        prob = np.exp(d)/(1+np.exp(d))
        pdv = np.array([1-prob, prob])
        pdv = pdv.T
        return pdv
