我们从Python开源项目中，提取了以下7个代码示例，用于说明如何使用scipy.stats.chi2()。
def correct_covariance(self, data): """Apply a correction to raw Minimum Covariance Determinant estimates. Correction using the empirical correction factor suggested by Rousseeuw and Van Driessen in [Rouseeuw1984]_. Parameters ---------- data : array-like, shape (n_samples, n_features) The data matrix, with p features and n samples. The data set must be the one which was used to compute the raw estimates. Returns ------- covariance_corrected : array-like, shape (n_features, n_features) Corrected robust covariance estimate. """ correction = np.median(self.dist_) / chi2(data.shape[1]).isf(0.5) covariance_corrected = self.raw_covariance_ * correction self.dist_ /= correction return covariance_corrected
def get_rv(self,signal): #extract parameters N=signal.S.shape[0] r,p=self.detector.A.shape if self.type=="PFA": if self.detector.estimate_sigma2==True: rv=f(r,N-p) else: rv=chi2(r) if self.type=="PD": #construct matrices A=np.matrix(self.detector.A) b=np.matrix(self.detector.b).T H=np.matrix(signal.H) x=np.matrix(signal.X) sigma2=signal.sigma2 #compute the non central parameter term1=A*x-b nc=term1.T*lg.inv(A*lg.inv(H.T*H)*A.T)*term1/sigma2 nc=min(np.asscalar(nc),self.nc_max) if self.detector.estimate_sigma2==True: rv=ncf(r,N-p,nc) else: rv=ncx2(r,nc) return rv ###### LINEAR MODEL WITH INTERFERENCE ##################################
def get_rv(self,signal): #extract parameters N,p=signal.H1.shape if self.type=="PFA": if self.detector.estimate_sigma2==True: rv=f(p,s-p) else: rv=chi2(p) if self.type=="PD": H1=np.matrix(signal.H1) x1=np.matrix(signal.x1).T H2=np.matrix(signal.H2) x2=np.matrix(signal.x2).T sigma2=signal.sigma2 #compute non central parameter PH2_perp=np.eye(H2.shape[0])-H2*lg.inv(H2.T*H2)*H2.T y=H1*x1 nc=y.T*PH2_perp*y/sigma2 nc=min(np.asscalar(nc),self.nc_max) if self.detector.estimate_sigma2==True: N,t=H2.shape s=N-t rv=ncf(p,s-p,nc) else: rv=ncx2(p,nc) return rv
def get_rv(self,signal): check_MD(signal.X) #extract parameters N=signal.N r,p=self.detector.A.shape if self.type=="PFA": if self.detector.estimate_sigma2==True: rv=f(r,N-p) else: rv=chi2(r) if self.type=="PD": #construct matrices A=np.matrix(self.detector.A) b=np.matrix(self.detector.b).T H=np.matrix(signal.H) x=np.matrix(signal.X) sigma2=signal.sigma2 #compute the non central parameter term1=A*x-b nc=term1.T*lg.inv(A*lg.inv(H.T*H)*A.T)*term1/sigma2 nc=min(np.asscalar(nc),self.nc_max) if self.detector.estimate_sigma2==True: rv=ncf(r,N-p,nc) else: rv=ncx2(r,nc) return rv ###### LINEAR MODEL WITH INTERFERENCE ##################################
def __init__(self, stat, null, df, df_denom=None, name=None): self._stat = stat self._null = null self.df = df self.df_denom = df_denom self._name = name if df_denom is None: self.dist = chi2(df) self.dist_name = 'chi2({0})'.format(df) else: self.dist = f(df, df_denom) self.dist_name = 'F({0},{1})'.format(df, df_denom)
def reweight_covariance(self, data): """Re-weight raw Minimum Covariance Determinant estimates. Re-weight observations using Rousseeuw's method (equivalent to deleting outlying observations from the data set before computing location and covariance estimates). [Rouseeuw1984]_ Parameters ---------- data : array-like, shape (n_samples, n_features) The data matrix, with p features and n samples. The data set must be the one which was used to compute the raw estimates. Returns ------- location_reweighted : array-like, shape (n_features, ) Re-weighted robust location estimate. covariance_reweighted : array-like, shape (n_features, n_features) Re-weighted robust covariance estimate. support_reweighted : array-like, type boolean, shape (n_samples,) A mask of the observations that have been used to compute the re-weighted robust location and covariance estimates. """ n_samples, n_features = data.shape mask = self.dist_ < chi2(n_features).isf(0.025) if self.assume_centered: location_reweighted = np.zeros(n_features) else: location_reweighted = data[mask].mean(0) covariance_reweighted = self._nonrobust_covariance( data[mask], assume_centered=self.assume_centered) support_reweighted = np.zeros(n_samples, dtype=bool) support_reweighted[mask] = True self._set_covariance(covariance_reweighted) self.location_ = location_reweighted self.support_ = support_reweighted X_centered = data - self.location_ self.dist_ = np.sum( np.dot(X_centered, self.get_precision()) * X_centered, 1) return location_reweighted, covariance_reweighted, support_reweighted
