def compute_statistic(self, alphahat, R, RA, N, Nref, memoize=False): # TODO: should we regularize RA? print("regularizing R...") Rreg = R.add_ridge(self.params.Lambda, renormalize=True) if not memoize or not hasattr(self, "bias"): print("done.computing bias...") self.bias = BlockDiag.solve(Rreg, RA).trace() / N print("bias =", self.bias) betahat = BlockDiag.solve(Rreg, alphahat) return betahat.dot(RA.dot(betahat)) - self.bias
def compute_statistic(self, alphahat, R, RA, N, Nref, memoize=False): #TODO: should we regularize RA? print('regularizing R...') Rreg = R.add_ridge(self.params.Lambda, renormalize=True) if not memoize or not hasattr(self, 'bias'): print('done.computing bias...') self.bias = BlockDiag.solve(Rreg, RA).trace() / N print('bias =', self.bias) betahat = BlockDiag.solve(Rreg, alphahat) return betahat.dot(RA.dot(betahat)) - self.bias
def compute_statistic(self, alphahat, R, RA, N, Nref, memoize=False): try: if not memoize or not hasattr(self, "bias"): print("computing bias") self.bias = BlockDiag.solve(R, RA).trace() / N print("bias =", self.bias) betahat = BlockDiag.solve(R, alphahat) return betahat.dot(RA.dot(betahat)) - self.bias except np.linalg.linalg.LinAlgError: print("R was singular. Its shape was", R.shape(), "and Nref=", Nref) return 0
def compute_statistic(self, alphahat, R, RA, N, Nref, memoize=False): try: if not memoize or not hasattr(self, 'bias'): print('computing bias') self.bias = BlockDiag.solve(R, RA).trace() / N print('bias =', self.bias) betahat = BlockDiag.solve(R, alphahat) return betahat.dot(RA.dot(betahat)) - self.bias except np.linalg.linalg.LinAlgError: print('R was singular. Its shape was', R.shape(), 'and Nref=', Nref) return 0