singleshot.py 文件源码

def logistic_fidelity(self):
        #group data and assign state labels
        gnd_features = np.hstack([np.real(self.ground_data.T),
                                np.imag(self.ground_data.T)])
        ex_features = np.hstack([np.real(self.excited_data.T),
                                np.imag(self.excited_data.T)])
        #liblinear wants arrays in C order
        features = np.ascontiguousarray(np.vstack([gnd_features, ex_features]))
        state = np.ascontiguousarray(np.hstack([np.zeros(self.ground_data.shape[1]),
                                                np.ones(self.excited_data.shape[1])]))
        #Set up logistic regression with cross-validation using liblinear.
        #Cs sets the inverse of the regularization strength, which will be optimized
        #through cross-validation. Uses the default Stratified K-Folds
        #CV generator, with 3 folds.
        #This is set up to be as consistent with the MATLAB implementation
        #as I can make it. --GJR
        Cs = np.logspace(-1,2,5)
        logreg = LogisticRegressionCV(Cs, cv=3, solver='liblinear')
        logreg.fit(features, state) #fit the model
        predictions = logreg.predict(features) #in-place classification
        score = logreg.score(features,state) #mean accuracy of classification
        N = len(predictions)
        S = np.sum(predictions == state) #how many we got right
        #now calculate confidence intervals
        c = 0.95
        flo = betaincinv(S+1, N-S+1, (1-c)/2., )
        fhi = betaincinv(S+1, N-S+1, (1+c)/2., )
        logger.info(("In-place logistic regression fidelity: " +
                "{:.2f}% ({:.2f}, {:.2f})".format(100*score, 100*flo, 100*fhi)))