support/models.py
def eval(self, w):
        '''
        Evaluate a parameter vector using oracle information
        on the true underlying objective. The form below assumes
        only that both the inputs and additive noise have zero
        mean, and that the inputs are independent of the noise.
        There is no restriction on correlation between the
        components of the input.
        '''
        diff = w - self.w_true
        quad = np.dot(diff.transpose(),\
                      np.dot(self.cov_X, diff).reshape(diff.shape))

        return np.float64(quad) + self.sigma_noise

        Evaluate a parameter vector using oracle information
        on the true underlying objective. The form below assumes
        only that both the inputs and additive noise have zero
        mean, and that the inputs are independent of the noise.
        There is no restriction on correlation between the
        components of the input.
        
Evaluate a parameter vector using oracle information
on the true underlying objective. The form below assumes
only that both the inputs and additive noise have zero
mean, and that the inputs are independent of the noise.
There is no restriction on correlation between the
components of the input.
Evaluate a parameter vector using oracle information on the true underlying objective . The form below assumes only that both the inputs and additive noise have zero mean and that the inputs are independent of the noise . There is no restriction on correlation between the components of the input .
def eval(self, w):
        diff = w - self.w_true
        quad = np.dot(diff.transpose(),\
                      np.dot(self.cov_X, diff).reshape(diff.shape))
        return np.float64(quad) + self.sigma_noise
def eval ( self , w ) : diff = w - self . w_true quad = np . dot ( diff . transpose ( ) , np . dot ( self . cov_X , diff ) . reshape ( diff . shape ) ) return np . float64 ( quad ) + self . sigma_noise
Evaluate a parameter vector using oracle information
on the true underlying objective.
Evaluate a parameter vector using oracle information on the true underlying objective .
'''
        Evaluate a parameter vector using oracle information
        on the true underlying objective. The form below assumes
        only that both the inputs and additive noise have zero
        mean, and that the inputs are independent of the noise.
        There is no restriction on correlation between the
        components of the input.
        '''
self
w