start_prob = np.zeros(n_states)
时间: 2023-06-12 08:05:00 浏览: 42
这代码看起来是在创建一个长度为 n_states 的全零数组 start_prob,用于表示起始状态的概率分布。在很多基于概率模型的问题中,我们需要指定一个起始状态,而这个起始状态的选择可能会影响到整个模型的预测结果。因此,我们可以使用一个概率分布来表示这些可能的起始状态,其中每个状态的概率就是 start_prob 数组中对应位置的值。这个数组可以通过不同的方式来初始化,比如均匀分布、高斯分布等等,具体取决于具体问题的特点。
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def compute(obs, states, start_p, trans_p, emit_p): # max_p(3*2)每一列存储第一列不同隐状态的最大概率 max_p = np.zeros((len(obs), len(states))) path = np.zeros((len(states), len(obs))) # 初始化 for i in range(len(states)): max_p[0][i] = start_p[i] * emit_p[i][obs[0]] path[i][0] = i for t in range(1, len(obs)): newpath = np.zeros((len(states), len(obs))) for y in range(len(states)): prob = -1 for y0 in range(len(states)): nprob = max_p[t-1][y0] * trans_p[y0][y] * emit_p[y][obs[t]] if nprob > prob: prob = nprob state = y0 # 记录路径 max_p[t][y] = prob for m in range(t): newpath[y][m] = path[state][m] newpath[y][t] = y请给这段代码每行代码加上注释
# 导入需要的库
import numpy as np
# 定义函数compute,传入参数obs观测序列、states隐状态、start_p初始概率、trans_p转移概率、emit_p发射概率
def compute(obs, states, start_p, trans_p, emit_p):
# max_p(3*2)每一列存储第一列不同隐状态的最大概率
max_p = np.zeros((len(obs), len(states)))
# path记录每个隐状态的路径
path = np.zeros((len(states), len(obs)))
# 初始化
for i in range(len(states)):
# 计算第一列不同隐状态的最大概率
max_p[0][i] = start_p[i] * emit_p[i][obs[0]]
# 记录第一列的路径
path[i][0] = i
# 对于后面的每个时刻
for t in range(1, len(obs)):
# 新建一个路径
newpath = np.zeros((len(states), len(obs)))
# 对于每个隐状态
for y in range(len(states)):
prob = -1
# 对于上一个时刻的每个隐状态
for y0 in range(len(states)):
# 计算概率
nprob = max_p[t-1][y0] * trans_p[y0][y] * emit_p[y][obs[t]]
# 如果概率更大
if nprob > prob:
prob = nprob
state = y0
# 记录路径
max_p[t][y] = prob
for m in range(t):
newpath[y][m] = path[state][m]
newpath[y][t] = y
# 更新路径
path = newpath
# 返回结果
return path, max_p
for i in range(len(states)): max_p[0][i] = start_p[i] * emit_p[i][obs[0]] path[i][0] = i for t in range(1, len(obs)): newpath = np.zeros((len(states), len(obs))) for y in range(len(states)): prob = -1 for y0 in range(len(states)): nprob = max_p[t-1][y0] * trans_p[y0][y] * emit_p[y][obs[t]] if nprob > prob: prob = nprob state = y0 # 记录路径 max_p[t][y] = prob for m in range(t): newpath[y][m] = path[state][m] newpath[y][t] = y path = newpath max_prob = -1 path_state = 0给这段代码加上详细注释
# 定义变量
states = ['Rainy', 'Sunny'] # 可能的状态
obs = ['walk', 'shop', 'clean'] # 观测序列
start_p = [0.6, 0.4] # 初始概率
trans_p = [[0.7, 0.3], [0.4, 0.6]] # 转移概率
emit_p = [[0.1, 0.4, 0.5], [0.6, 0.3, 0.1]] # 发射概率
# 初始化 max_p 和 path
max_p = np.zeros((len(obs), len(states)))
path = np.zeros((len(states), len(obs)))
# 初始化 max_p 和 path 的第一列
for i in range(len(states)):
max_p[0][i] = start_p[i] * emit_p[i][obs[0]]
path[i][0] = i
# 遍历观测序列
for t in range(1, len(obs)):
# 创建新路径
newpath = np.zeros((len(states), len(obs)))
# 遍历可能的状态
for y in range(len(states)):
prob = -1
# 遍历上一时刻的所有状态
for y0 in range(len(states)):
# 计算概率
nprob = max_p[t-1][y0] * trans_p[y0][y] * emit_p[y][obs[t]]
# 如果概率更大,更新 prob 和 state,并记录路径
if nprob > prob:
prob = nprob
state = y0
max_p[t][y] = prob
for m in range(t):
newpath[y][m] = path[state][m]
# 记录当前状态
newpath[y][t] = y
# 更新路径和最大概率
path = newpath
max_prob = -1
path_state = 0