Source code for pydelfi.priors

from scipy.stats import multivariate_normal
import numpy as np

class TruncatedGaussian():

    def __init__(self, mean, C, lower, upper):

        self.mean = mean
        self.C = C
        self.Cinv = np.linalg.inv(C)
        self.lower = lower
        self.upper = upper
        self.L = np.linalg.cholesky(C)
        self.logdet = np.log(np.linalg.det(C))
        
    def loguniform(self, x):

        inrange = np.prod(x > self.lower)*np.prod(x < self.upper)
        return inrange*np.log(np.prod(self.upper-self.lower)) - (1 - inrange)*1e300
    
    def uniform(self, x):
    
        inrange = np.prod(x > self.lower)*np.prod(x < self.upper)
        return inrange*np.prod(self.upper-self.lower)

    def draw(self):

        P = 0
        while P == 0:
            x = self.mean + np.dot(self.L, np.random.normal(0, 1, len(self.mean)))
            P = self.uniform(x)
        return x

    def pdf(self, x):

        return np.exp(self.logpdf(x))

    def logpdf(self, x):
        
        return np.array([self.loguniform(xx) - 0.5*self.logdet - 0.5*np.dot((xx - self.mean), np.dot(self.Cinv,(xx - self.mean)) ) for xx in x])


class Uniform():

    def __init__(self, lower, upper):

        self.lower = lower
        self.upper = upper

    def logpdf(self, x):

        inrange = lambda y: np.prod(y > self.lower)*np.prod(y < self.upper)
        return np.array([inrange(xx)*np.log(np.prod(self.upper-self.lower)) - (1 - inrange(xx))*1e300 for xx in x])
    
    def pdf(self, x):
        
        inrange = lambda y: np.prod(y > self.lower)*np.prod(y < self.upper)
        return np.array([inrange(xx)*np.prod(self.upper-self.lower) for xx in x])

    def draw(self):

        return np.random.uniform(self.lower, self.upper)