Building Machine Learning Systems with Python 1

import scipy as sp
data = sp.genfromtxt("web_traffic.tsv", delimiter="	")
x = data[:,0]
y = data[:,1]
x = x[~sp.isnan(y)]
y = y[~sp.isnan(y)]
import matplotlib.pyplot as plt
plt.scatter(x,y)
plt.title("Web traffic over the last month")
plt.xlabel("Time")
plt.ylabel("Hits/hour")
plt.xticks([w*7*24 for w in range(10)],
           ['week %i'%w for w in range(10)])

def error(f,x,y):
    return sp.sum((f(x)-y)**2)

fp1, residuals, rank, sv, rcomd = sp.polyfit(x,y,1,full=True)
f1 = sp.poly1d(fp1)
fx = sp.linspace(0,x[-1], 1000)
plt.plot(fx, f1(fx), linewidth=4)
plt.legend(["d=%i" % f1.order], loc="upper left")

f2p = sp.polyfit(x, y, 2)
f2 = sp.poly1d(f2p)
fx = sp.linspace(0,x[-1], 1000)
plt.plot(fx, f2(fx), linewidth=4)
plt.legend(["d=%i" % f2.order], loc="upper left")

f3p = sp.polyfit(x, y, 3)
f3 = sp.poly1d(f3p)
fx = sp.linspace(0,x[-1], 1000)
plt.plot(fx, f3(fx), linewidth=4)
plt.legend(["d=%i" % f3.order], loc="upper left")

plt.autoscale(tight=True)
plt.grid()
plt.show()

Note:

1>polyfit()  在图中画出要拟合的线。给定数据x，y 以及期望的多项式阶，它可以找到一个模型,并且返回函数模型的参数。

2>poly1d() 根据参数创建模型函数。