隐马尔科夫模型 使用（下）

# !/usr/bin/python
# -*- coding:utf-8 -*-

import numpy as np
from hmmlearn import hmm
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn.metrics.pairwise import pairwise_distances_argmin
import warnings


def expand(a, b):
    d = (b - a) * 0.05
    return a-d, b+d


if __name__ == "__main__":
    warnings.filterwarnings("ignore")   # hmmlearn(0.2.0) < sklearn(0.18)
    np.random.seed(0)

    n = 5   # 隐状态数目
    n_samples = 1000
    pi = np.random.rand(n)
    pi /= pi.sum()
    print('初始概率：', pi)

    A = np.random.rand(n, n)
    mask = np.zeros((n, n), dtype=np.bool)
    mask[0][1] = mask[0][4] = True
    mask[1][0] = mask[1][2] = True
    mask[2][1] = mask[2][3] = True
    mask[3][2] = mask[3][4] = True
    mask[4][0] = mask[4][3] = True
    A[mask] = 0
    for i in range(n):
        A[i] /= A[i].sum()
    print('转移概率：\n', A)

    means = np.array(((30, 30), (0, 50), (-25, 30), (-15, 0), (15, 0)))
    print('均值：\n', means)

    covars = np.empty((n, 2, 2))
    for i in range(n):
        # covars[i] = np.diag(np.random.randint(1, 5, size=2))
        covars[i] = np.diag(np.random.rand(2)+0.001)*30    # np.random.rand ∈[0,1)
    print('方差：\n', covars)

    model = hmm.GaussianHMM(n_components=n, covariance_type='full')
    model.startprob_ = pi
    model.transmat_ = A
    model.means_ = means
    model.covars_ = covars
    sample, labels = model.sample(n_samples=n_samples, random_state=0)

    # 估计参数
    model = hmm.GaussianHMM(n_components=n, covariance_type='full', n_iter=10)
    model = model.fit(sample)
    y = model.predict(sample)
    np.set_printoptions(suppress=True)
    print('##估计初始概率：', model.startprob_)
    print('##估计转移概率：\n', model.transmat_)
    print('##估计均值：\n', model.means_)
    print('##估计方差：\n', model.covars_)

    # 类别
    order = pairwise_distances_argmin(means, model.means_, metric='euclidean')
    print(order)
    pi_hat = model.startprob_[order]
    A_hat = model.transmat_[order]
    A_hat = A_hat[:, order]
    means_hat = model.means_[order]
    covars_hat = model.covars_[order]
    change = np.empty((n, n_samples), dtype=np.bool)
    for i in range(n):
        change[i] = y == order[i]
    for i in range(n):
        y[change[i]] = i
    print('估计初始概率：', pi_hat)
    print('估计转移概率：\n', A_hat)
    print('估计均值：\n', means_hat)
    print('估计方差：\n', covars_hat)
    print(labels)
    print(y)
    acc = np.mean(labels == y) * 100
    print('准确率：%.2f%%' % acc)

    mpl.rcParams['font.sans-serif'] = ['SimHei']
    mpl.rcParams['axes.unicode_minus'] = False
    plt.scatter(sample[:, 0], sample[:, 1], s=20, c=labels, cmap=plt.cm.Spectral, marker='o', edgecolors='k',
                label='观测值', linewidths=0.5, zorder=20)
    plt.plot(sample[:, 0], sample[:, 1], 'r-', zorder=10, lw=0.3)
    plt.scatter(means[:, 0], means[:, 1], s=60, c=np.random.rand(n), marker='D', label='中心', alpha=0.8, zorder=30)
    x1_min, x1_max = sample[:, 0].min(), sample[:, 0].max()
    x2_min, x2_max = sample[:, 1].min(), sample[:, 1].max()
    x1_min, x1_max = expand(x1_min, x1_max)
    x2_min, x2_max = expand(x2_min, x2_max)
    plt.xlim((x1_min, x1_max))
    plt.ylim((x2_min, x2_max))
    plt.legend(loc='upper left')
    plt.grid(True, ls=':', color='#505050')
    plt.title('高斯分布的隐马尔可夫模型样本状态预测', fontsize=14)
    plt.show()

# !/usr/bin/python
# -*- coding:utf-8 -*-

import numpy as np
from hmmlearn import hmm
import matplotlib.pyplot as plt
import matplotlib as mpl
# from sklearn.metrics.pairwise import pairwise_distances_argmin
import warnings


def expand(a, b):
    d = (b - a) * 0.05
    return a-d, b+d


if __name__ == "__main__":
    warnings.filterwarnings("ignore")   # hmmlearn(0.2.0) < sklearn(0.18)

    # 0日期  1开盘  2最高  3最低  4收盘  5成交量  6成交额
    x = np.loadtxt('SH600000.txt', delimiter='\t', skiprows=2, usecols=(4, 5, 6, 2, 3))
    close_price = x[:, 0]
    volumn = x[:, 1]
    amount = x[:, 2]
    amplitude_price = x[:, 3] - x[:, 4] # 每天的最高价与最低价的差
    diff_price = np.diff(close_price)   # 涨跌值
    volumn = volumn[1:]                 # 成交量
    amount = amount[1:]                 # 成交额
    amplitude_price = amplitude_price[1:]   # 每日振幅
    sample = np.column_stack((diff_price, volumn, amount, amplitude_price))    # 观测值
    n = 5
    model = hmm.GaussianHMM(n_components=n, covariance_type='full')
    model.fit(sample)
    y = model.predict_proba(sample)
    np.set_printoptions(suppress=True)
    print(y)

    t = np.arange(len(diff_price))
    mpl.rcParams['font.sans-serif'] = ['SimHei']
    mpl.rcParams['axes.unicode_minus'] = False
    plt.figure(figsize=(10,7), facecolor='w')
    plt.subplot(421)
    plt.plot(t, diff_price, 'r-', lw=0.7)
    plt.grid(True)
    plt.title('涨跌幅')
    plt.subplot(422)
    plt.plot(t, volumn, 'g-', lw=0.7)
    plt.grid(True)
    plt.title('交易量')

    clrs = plt.cm.terrain(np.linspace(0, 0.8, n))
    plt.subplot(423)
    for i, clr in enumerate(clrs):
        plt.plot(t, y[:, i], '-', color=clr, alpha=0.7, lw=0.7)
    plt.title('所有组分')
    plt.grid(True)
    for i, clr in enumerate(clrs):
        axes = plt.subplot(4, 2, i+4)
        plt.plot(t, y[:, i], '-', color=clr, lw=0.7)
        plt.title('组分%d' % (i+1))
        plt.grid(True)
    plt.suptitle('SH600000股票：GaussianHMM分解隐变量', fontsize=18)
    plt.tight_layout()
    plt.subplots_adjust(top=0.9)
    plt.show()