distributions.py 文件源码

def log_likelihood(self,x,r=None,p=None):
        r = r if r is not None else self.r
        p = p if p is not None else self.p
        x = np.array(x,ndmin=1)

        if self.p > 0:
            xnn = x[x >= 0]
            raw = np.empty(x.shape)
            raw[x>=0] = special.gammaln(r + xnn) - special.gammaln(r) \
                    - special.gammaln(xnn+1) + r*np.log(1-p) + xnn*np.log(p)
            raw[x<0] = -np.inf
            return raw if isinstance(x,np.ndarray) else raw[0]
        else:
            raw = np.log(np.zeros(x.shape))
            raw[x == 0] = 0.
            return raw if isinstance(x,np.ndarray) else raw[0]