本文整理汇总了Python中hmmlearn.hmm.GaussianHMM.sample方法的典型用法代码示例。如果您正苦于以下问题：Python GaussianHMM.sample方法的具体用法？Python GaussianHMM.sample怎么用？Python GaussianHMM.sample使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类hmmlearn.hmm.GaussianHMM的用法示例。
在下文中一共展示了GaussianHMM.sample方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: bench_gaussian_hmm
# 需要导入模块: from hmmlearn.hmm import GaussianHMM [as 别名]
# 或者: from hmmlearn.hmm.GaussianHMM import sample [as 别名]
def bench_gaussian_hmm(size):
title = "benchmarking Gaussian HMM on a sample of size {0}".format(size)
print(title.center(36, " "))
ghmm = GaussianHMM()
ghmm.means_ = [[42], [24]]
ghmm.covars_ = [[1], [1]]
with timed_step("generating sample"):
sample, _states = ghmm.sample(size)
with timed_step("fitting"):
fit = GaussianHMM(n_components=2).fit([sample])
with timed_step("estimating states"):
fit.predict(sample)示例2: print
# 需要导入模块: from hmmlearn.hmm import GaussianHMM [as 别名]
# 或者: from hmmlearn.hmm.GaussianHMM import sample [as 别名]
import numpy as np
import matplotlib.pyplot as plt
from hmmlearn.hmm import GaussianHMM
# 从输入文件中加载数据
input_file = 'CNY.csv'
data = np.loadtxt(input_file, delimiter=',')
# 提取需要的值
closing_values = np.array(data[:, 6])
volume_of_shares = np.array(data[:, 8])[:-1]
# 计算每天收盘价变化率
diff_percentage = 100.0 * np.diff(closing_values) / closing_values[:-1]
# 将变化率与交易量组合起来
X = np.column_stack((diff_percentage, volume_of_shares))
# 创建并训练高斯隐马尔科夫模型
print(u"训练高斯隐马尔科夫模型中......")
model = GaussianHMM(n_components=5, covariance_type='diag', n_iter=1000)
model.fit(X)
# 用模型生成数据
num_samples = 500
samples, _ = model.sample(num_samples)
plt.plot(np.arange(num_samples), samples[:, 0], c='black')
plt.figure()
plt.plot(np.arange(num_samples), samples[:, 1], c='red')
plt.show()
示例3: of
# 需要导入模块: from hmmlearn.hmm import GaussianHMM [as 别名]
# 或者: from hmmlearn.hmm.GaussianHMM import sample [as 别名]
mean = np.array([[0.0, 0.0],
[0.0, 10.0],
[10.0, 0.0]])
# Setting the mean
model_gaussian.means_ = mean
# As emission probability is a 2-D gaussian distribution, thus
# covariance matrix for each state would be a 2-D matrix, thus
# overall the covariance matrix for all the states would be in the
# form of (n_components, 2, 2)
covariance = 0.5 * np.tile(np.identity(2), (3, 1, 1))
model_gaussian.covars_ = covariance
# model.sample returns both observations as well as hidden states
# the first return argument being the observation and the second
# being the hidden states
Z, X = model_gaussian.sample(100)
# Plotting the observations
plt.plot(Z[:, 0], Z[:, 1], "-o", label="observations",
ms=6, mfc="orange", alpha=0.7)
# Indicate the state numbers
for i, m in enumerate(mean):
plt.text(m[0], m[1], 'Component %i' % (i + 1),
size=17, horizontalalignment='center',
bbox=dict(alpha=.7, facecolor='w'))
plt.legend(loc='best')
plt.show()