data.py 文件源码

def gmm_sample(self, mean=None, w=None, N=10000,n=10,d=2,seed=10):
        np.random.seed(seed)
        self.d = d
        if mean is None:
            mean = np.random.randn(n,d)*10
        if w is None:
            w = np.random.rand(n)
        w = w/sum(w)
        multi = np.random.multinomial(N,w)
        X = np.zeros((N,d))
        base = 0
        for i in range(n):
            X[base:base+multi[i],:] = np.random.multivariate_normal(mean[i,:], np.eye(self.d), multi[i])
            base += multi[i]

        llh = np.zeros(N)
        for i in range(n):
            llh += w[i] * stats.multivariate_normal.pdf(X, mean[i,:], np.eye(self.d))
        #llh = llh/sum(llh)
        return X, llh