GMMHMM.score

GMMHMM.score是基于GMM-HMM（高斯混合模型-隐马尔可夫模型）的概率计算方法。它用于计算给定观测序列的概率，可以用于语音识别、手写识别等领域。在语音识别中，GMM-HMM模型用于将声学特征与文字转换为一个概率分布，以便于识别出最可能的语音识别结果。在手写识别中，GMM-HMM模型用于将手写笔迹转换为一个概率分布，以便于识别出最可能的文字结果。

相关问题

import sys from hmmlearn.hmm import MultinomialHMM import numpy as np dice_num = 3 x_num = 8 dice_hmm = MultinomialHMM(n_components=3,n_features=8,n_trials=5) dice_hmm.startprob_ = np.ones(3) / 3.0 dice_hmm.transmat_ = np.ones((3, 3)) / 3.0 dice_hmm.emissionprob_ = np.array([[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]]) dice_hmm.emissionprob_ /= dice_hmm.emissionprob_.sum(axis=1)[:, np.newaxis] X = np.array([[0], [5], [2], [4], [1], [6], [2], [4], [1], [3], [2], [5], [0], [4], [3]]) Z = dice_hmm.decode(X) # 问题A logprob = dice_hmm.score(X) # 问题B # 问题C x_next = np.dot(dice_hmm.transmat_, dice_hmm.emissionprob_) print("state: ", Z) print("logprob: ", logprob) print("prob of x_next: ", x_next)请修改和完善以上代码

import sys
from hmmlearn.hmm import MultinomialHMM
import numpy as np

dice_num = 3
x_num = 8

dice_hmm = MultinomialHMM(n_components=3, n_features=8)

dice_hmm.startprob_ = np.ones(3) / 3.0
dice_hmm.transmat_ = np.ones((3, 3)) / 3.0
dice_hmm.emissionprob_ = np.array([[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
[1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]])
dice_hmm.emissionprob_ /= dice_hmm.emissionprob_.sum(axis=1)[:, np.newaxis]

X = np.array([[0], [5], [2], [4], [1], [6], [2], [4], [1], [3], [2], [5], [0], [4], [3]])

# 问题A
Z = dice_hmm.predict(X)

# 问题B
logprob = dice_hmm.score(X)

# 问题C
x_next = np.dot(dice_hmm.transmat_, dice_hmm.emissionprob_)

print("state: ", Z)
print("logprob: ", logprob)
print("prob of x_next: ", x_next)

aver = (s[i].score[0], s[i].score[1], s[i].score[2], s[i].score[3], s[i].score[4], s[i].score[5], s[i].aver); s[i].score[0] + s[i].score[1] + s[i].score[2] + s[i].score[3] + s[i].score[4] + s[i].score[5]) / 6;哪里错了

这段代码中有一个语法错误，缺少了分号。正确的代码应该是：

aver = (s[i].score[0], s[i].score[1], s[i].score[2], s[i].score[3], s[i].score[4], s[i].score[5], s[i].score[0] + s[i].score[1] + s[i].score[2] + s[i].score[3] + s[i].score[4] + s[i].score[5]) / 6;

另外，这段代码还存在逻辑错误，应该将求平均分的部分放在括号内，正确的代码应该是：

aver = (s[i].score[0] + s[i].score[1] + s[i].score[2] + s[i].score[3] + s[i].score[4] + s[i].score[5]) / 6;