Python机器学习ufunc函数

# -*- coding: utf-8 -*-__author__ = 'gerry'import numpy as npimport matplotlib.pyplot as pltimport mathimport datetimefrom mpl_toolkits.mplot3d import Axes3D# ufunc函数# ufunc韩式是一种能对数组的每个元素进行运算的函数,Numpy内置的很多ufunc都是用C语言实现的x1 = np.linspace(0,2*np.pi,10)y = np.sin(x1)plt.rcParams['lines.color']='r'plt.plot(x1,y)# plt.show()print "====================================================="x = [i*0.001 for i in xrange(1000000)]def sin_math(x):    for i,t in enumerate(x):        x[i] = math.sin(t)def sin_numpy(x):    np.sin(x,x)def sin_numpy_loop(x):    for i,t in enumerate(x):        x[i]=np.sin(t)xl = x[:]starttime = datetime.datetime.now()sin_math(xl)endtime = datetime.datetime.now()print(endtime-starttime).secondsxa = np.array(x)starttime1 = datetime.datetime.now()sin_numpy(xa)endtime1 = datetime.datetime.now()print(endtime1-starttime1).secondsxj = x[:]starttime2 = datetime.datetime.now()sin_numpy_loop(xj)endtime2 = datetime.datetime.now()print(endtime2-starttime2).secondsprint "====================================================="# 2.2.1四则运算# Numpy提供了许多ufunc函数，例如计算两个数组之和的add()函数a = np.arange(0,4)b = np.arange(1,5)print aprint np.add(a,b)np.multiply(a,b,a)print a#数组的运算符以及对应的ufunc函数#y = x1+x2:add(x1,x2,y)#y = x1-x2:subtract(x1,x2,y)#y = x1*x2:myltiply(x1,x2,y)#y = x1/x2:divide(x1,x2,y),如果两个数组的元素为整数，那么用整除除法#y = x1/x2:true_divide(x1,x2,y),总是返回精确的值#y = x1//x2:add(x1,x2,y)，总是对返回值取整#y = -x:negative(x,y)#y = x1**x2:power(x1,x2,y)#y = x1%x2:mod(x1,x2,y)print "====================================================="#2.2.2比较运算符合布尔运算result = np.array([1,2,3])<np.array([3,2,1])print result#比较运算符和相应的ufunc函数#y = x1==x2:equal(x1,x2,y)#y = x1!=x2:not_equal(x1,x2,y)#y = x1<x2:less(x1,x2,y)#y = x1<=x2:less_equal(x1,x2,y)#y = x1>x2:greater(x1,x2,y)#y = x1>=x2:greater_equal(x1,x2,y)# 由于Python中的布尔运算符and、or和not等关键字，无法被重载，因此数组的布尔运算符有只能通过ufunc函数运行# np.logical_and(),np.logical_not(),np.logical_or()a1 = np.arange(5)b1 = np.arange(4,-1,-1)print a1>b1print np.logical_or(a1==b1,a1>b1) #和a1>=b1相同print np.any(a1==b1)print "====================================================="# 2.2.4广播# 当使用ufunc函数对两个数组进行计算时，ufunc函数会对这两个数组的对应元素进行计算# 因此它要求这两个数组的形状相同，如果形状不同，会进行如下广播（broadcasting）处理a2 = np.arange(0,60,10).reshape(-1,1)b2 = np.arange(0,5)c = a2+b2print cfig = plt.figure()ax = Axes3D(fig)x,y = np.ogrid[-2:2:20j,-2:2:20j]z = x*np.exp(-x**2-y**2)ax.plot_surface(x,y,z)plt.show()