GaussianHMM和ensemble.bagging的例程

末蓝、 2022-02-25 00:24 193阅读 0赞

import numpy as np
    import matplotlib.pyplot as plt
    import matplotlib as mpl
    from sklearn.linear_model import RidgeCV, LassoCV
    from sklearn.model_selection import train_test_split
    from sklearn.ensemble import BaggingRegressor
    from sklearn.tree import DecisionTreeRegressor
    from sklearn.pipeline import Pipeline
    from sklearn.preprocessing import PolynomialFeatures
    import pandas as pd
    import csv
    
    
    def f(x):
        return 0.5*np.exp(-(x+3) **2) + np.exp(-x**2) + + 0.5*np.exp(-(x-3) ** 2)
    
    if __name__ == "__main__":
        np.random.seed(0)
        N = 200
        x = np.random.rand(N) * 10 - 5  # [-5,5),将数组的元素值控制在一定的范围之内,限定数组元素的定义域
        x = np.sort(x)   #对数组元素进行排序
        y = f(x) + 0.05*np.random.randn(N)   #引入高斯随机噪声，增强模型鲁棒性和泛化性能
        x.shape = -1, 1
    
        ridge = RidgeCV(alphas=np.logspace(-3, 2, 10), fit_intercept=False)
        ridged = Pipeline([('poly', PolynomialFeatures(degree=10)), ('Ridge', ridge)])
        bagging_ridged = BaggingRegressor(ridged, n_estimators=100, max_samples=0.3)
        dtr = DecisionTreeRegressor(max_depth=5)
        regs = [
            ('DecisionTree Regressor', dtr),
            ('Ridge Regressor(6 Degree)', ridged),
            ('Bagging Ridge(6 Degree)', bagging_ridged),
            ('Bagging DecisionTree Regressor', BaggingRegressor(dtr, n_estimators=100, max_samples=0.3))]
        x_test = np.linspace(1.1*x.min(), 1.1*x.max(), 1000)
        mpl.rcParams['font.sans-serif'] = [u'SimHei']
        mpl.rcParams['axes.unicode_minus'] = False
        plt.figure(figsize=(12, 8), facecolor='w')
        plt.plot(x, y, 'ro', label=u'训练数据')
        plt.plot(x_test, f(x_test), color='k', lw=3.5, label=u'真实值')
        clrs = 'bmyg'
        for i, (name, reg) in enumerate(regs):
            reg.fit(x, y)
            y_test = reg.predict(x_test.reshape(-1, 1))
            plt.plot(x_test, y_test.ravel(), color=clrs[i], lw=i+1, label=name, zorder=6-i)
        plt.legend(loc='upper left')
        plt.xlabel('X', fontsize=15)
        plt.ylabel('Y', fontsize=15)
        plt.title(u'回归曲线拟合', fontsize=21)
        plt.ylim((-0.2, 1.2))
        plt.tight_layout(2)
        plt.grid(True)
        plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2pwX3pob3UyNTY_size_16_color_FFFFFF_t_70]![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2pwX3pob3UyNTY_size_16_color_FFFFFF_t_70 1]![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2pwX3pob3UyNTY_size_16_color_FFFFFF_t_70 2]

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,2-1,3-2,...
        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,数组(行向量)的竖直方向的堆叠
        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'] = [u'SimHei']
        mpl.rcParams['axes.unicode_minus'] = False
        plt.figure(figsize=(10,8), facecolor='w')
        plt.subplot(421)
        plt.plot(t, diff_price, 'r-')  #前后两天的价格差
        plt.grid(True)
        plt.title(u'涨跌幅')
        plt.subplot(422)
        plt.plot(t, volumn, 'g-')
        plt.grid(True)
        plt.title(u'交易量')
    
        clrs = plt.cm.terrain(np.linspace(0, 0.8, n))
        plt.subplot(423)
        for i, clr in enumerate(clrs):
            print(clr)
            plt.plot(t, y[:, i], '-', color=clr, alpha=0.7)
        plt.title(u'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)
            plt.title(u'组分%d' % (i+1))
            plt.grid(True)
        plt.suptitle(u'SH600000股票：GaussianHMM分解隐变量', fontsize=18)
        plt.tight_layout()
        plt.subplots_adjust(top=0.9)
        plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2pwX3pob3UyNTY_size_16_color_FFFFFF_t_70 3]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2pwX3pob3UyNTY_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/20190331092939124.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2pwX3pob3UyNTY=,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2pwX3pob3UyNTY_size_16_color_FFFFFF_t_70 1]: https://img-blog.csdnimg.cn/20190331093002855.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2pwX3pob3UyNTY=,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2pwX3pob3UyNTY_size_16_color_FFFFFF_t_70 2]: /images/20220225/5b52496e2e5546ae9bbb091020910c5f.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2pwX3pob3UyNTY_size_16_color_FFFFFF_t_70 3]: /images/20220225/76c7585769184c6a9fcee599b7931ae4.png