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()
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()