def emp_quantiles(self, X, quantiles=arange(0.1, 1, 0.1)): norms = array([norm(x) for x in X]) angles = arctan2(X[:, 1], X[:, 0]) mu = self.radius + self.amplitude * cos(self.frequency * angles) transformed = hstack((array([norms-mu]).T, X[:,2:self.dimension])) cov=eye(self.dimension-1) cov[0,0]=self.variance gaussian=Gaussian(zeros([self.dimension-1]), cov) return gaussian.emp_quantiles(transformed)
def emp_quantiles(self, X, quantiles=arange(0.1, 1, 0.1)): norms = array([norm(x) for x in X]) angles = arctan2(X[:, 1], X[:, 0]) mu = self.radius + self.amplitude * cos(self.frequency * angles) transformed = hstack((array([norms - mu]).T, X[:, 2:self.dimension])) cov = eye(self.dimension - 1) cov[0, 0] = self.variance gaussian = Gaussian(zeros([self.dimension - 1]), cov) return gaussian.emp_quantiles(transformed)
def emp_quantiles(X, bananicity=0.03, V=100, quantiles=np.arange(0.1, 1, 0.1)): assert(len(X.shape) == 2) D = X.shape[1] substract=bananicity * ((X[:, 0] ** 2) - V) divide=np.sqrt(V) X[:, 1] -= substract X[:, 0] /= divide phi = Gaussian(np.zeros(D), np.eye(D)) quantiles=phi.emp_quantiles(X, quantiles) # undo changes to X X[:, 0] *= divide X[:, 1] += substract return quantiles
def emp_quantiles(self, X, quantiles=arange(0.1, 1, 0.1)): assert (len(shape(X)) == 2) assert (shape(X)[1] == self.dimension) substract = self.bananicity * ((X[:, 0]**2) - self.V) divide = sqrt(self.V) X[:, 1] -= substract X[:, 0] /= divide phi = Gaussian(zeros([self.dimension]), eye(self.dimension)) quantiles = phi.emp_quantiles(X, quantiles) # undo changes to X X[:, 0] *= divide X[:, 1] += substract return quantiles
def emp_quantiles(X, bananicity=0.03, V=100, quantiles=np.arange(0.1, 1, 0.1)): assert (len(X.shape) == 2) D = X.shape[1] substract = bananicity * ((X[:, 0]**2) - V) divide = np.sqrt(V) X[:, 1] -= substract X[:, 0] /= divide phi = Gaussian(np.zeros(D), np.eye(D)) quantiles = phi.emp_quantiles(X, quantiles) # undo changes to X X[:, 0] *= divide X[:, 1] += substract return quantiles
def emp_quantiles(self, X, quantiles=arange(0.1, 1, 0.1)): assert(len(shape(X)) == 2) assert(shape(X)[1] == self.dimension) substract=self.bananicity * ((X[:, 0] ** 2) - self.V) divide=sqrt(self.V) X[:, 1] -= substract X[:, 0] /= divide phi = Gaussian(zeros([self.dimension]), eye(self.dimension)) quantiles=phi.emp_quantiles(X, quantiles) # undo changes to X X[:, 0] *= divide X[:, 1] += substract return quantiles