#58 Polynomial basis function

import os
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import make_pipeline
sns.set()
os.chdir(r'D:\projects\wordpress\ex58')
os.getcwd()
x = np.array([2, 3, 4])
poly = PolynomialFeatures(3, include_bias=False)
poly.fit_transform(x[:, None])
poly_model = make_pipeline(PolynomialFeatures(7),LinearRegression())
rng = np.random.RandomState(1)
x = 10 * rng.rand(50)
y = np.sin(x) + 0.1 * rng.randn(50)
poly_model.fit(x[:, np.newaxis], y)
xfit = np.linspace(0, 10, 1000)
yfit = poly_model.predict(xfit[:, np.newaxis])
plt.scatter(x,y,color='blue')
plt.plot(xfit,yfit,color='red')
plt.savefig("example58.png", dpi=100)
plt.show()
plt.close()