[R]中的多项式聚类：循环错误“缺少需要TRUE / FALSE的值”

Question

我正在尝试实施EM算法。我正在使用while循环进行迭代，而输出矩阵的变化大于某个阈值，但是我收到一条错误，说“同时出错（delta> = tau）{：缺少值需要TRUE / FALSE”下面是我的代码。

Multinomial<-function(H,K,tau) {
H=H+.01
count = 0
a_mat = 0
a_data = list()     #list to store old and new A matrices
delta = Inf     #initialize delta

#initialize mix weights and centroid matrices

c_mat = matrix(1/K,nrow(H),K)
rand_row = H[sample(1:40000,1),]        
t_mat = matrix(rand_row/sum(rand_row),ncol(H),1)
for(i in 1:(K-1)) {
    rand_row = H[sample(1:40000,1),]
    t_mat = cbind(t_mat,rand_row/sum(rand_row))
}   

while(delta>=tau) {
    count=count + 1
    a_data[[1]] = 0         #initialize for new A matrix
    a_data[[2]] = a_mat     #old A matrix

    #E-Step: computes assignment probability matrix
    phi_mat = exp(H%*%log(t_mat))       
    num_mat = (c_mat*phi_mat)         #numerator of a
    num_mat[num_mat==NaN]<-.00000001
    nrsums=rowSums(num_mat)
    a_mat = num_mat/nrsums  #assignment prob matrix
    a_data[[1]] = a_mat

    #M-Step: compute new mix weights and centroids
    acol_sum=.colSums(a_mat,nrow(a_mat),ncol(a_mat),na.rm=T)
    c_k = acol_sum/nrow(H)
    c_mat = matrix(c_k,nrow(H),K)
    b_mat = t(H)%*%a_mat
    t_mat = b_mat/colSums(b_mat)

    #compute measure of change (delta)
    if(count>1){
        delta = norm(a_data[[1]]-a_data[[2]],'O')
    }   
}