def mechanism(self, x):
        """Mechanism function."""
        self.nb_step += 1
        x = np.reshape(x, (x.shape[0], 1))

        if(self.nb_step < 5):
            cov = computeGaussKernel(x)
            mean = np.zeros((1, self.points))[0, :]
            y = np.random.multivariate_normal(mean, cov)
        elif(self.nb_step == 5):
            cov = computeGaussKernel(x)
            mean = np.zeros((1, self.points))[0, :]
            y = np.random.multivariate_normal(mean, cov)
            self.gpr = GaussianProcessRegressor()
            self.gpr.fit(x, y)
            y = self.gpr.predict(x)
        else:
            y = self.gpr.predict(x)

        return y
def mechanism(self, x):
        """Mechanism function."""
        self.nb_step += 1
        x = np.reshape(x, (x.shape[0], 1))

        if(self.nb_step < 5):
            cov = computeGaussKernel(x)
            mean = np.zeros((1, self.points))[0, :]
            y = np.random.multivariate_normal(mean, cov)
        elif(self.nb_step == 5):
            cov = computeGaussKernel(x)
            mean = np.zeros((1, self.points))[0, :]
            y = np.random.multivariate_normal(mean, cov)
            self.gpr = GaussianProcessRegressor()
            self.gpr.fit(x, y)
            y = self.gpr.predict(x)
        else:
            y = self.gpr.predict(x)

        return y
def mechanism ( self , x ) : self . nb_step += 1 x = np . reshape ( x , ( x . shape [ 0 ] , 1 ) ) if ( self . nb_step < 5 ) : cov = computeGaussKernel ( x ) mean = np . zeros ( ( 1 , self . points ) ) [ 0 , : ] y = np . random . multivariate_normal ( mean , cov ) elif ( self . nb_step == 5 ) : cov = computeGaussKernel ( x ) mean = np . zeros ( ( 1 , self . points ) ) [ 0 , : ] y = np . random . multivariate_normal ( mean , cov ) self . gpr = GaussianProcessRegressor ( ) self . gpr . fit ( x , y ) y = self . gpr . predict ( x ) else : y = self . gpr . predict ( x ) return y
Mechanism function.
Mechanism function .