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
