学习如逆水行舟，不进则退！
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2018-2-3
1、安装Anaconda，使用3.X版本进行学习。
2018-2-8
学习numpy的接口用法；
import numpytest_log = numpy.genfromtxt("testlog.txt", delimiter=",")print(test_log)is_value_empty = numpy.isnan(test_log[:,1])test_log[is_value_empty,1] ='0'test_2lie = test_log[:,1]allnum = test_2lie.astype(float)print(allnum.sum())print(allnum.mean())2018-2-12 练习矩阵bool型操作
2018-2-27 内积
a = np.array([[0, 3], [1, 4]])b = np.array([[2, 0], [1, 6]])#print(a*b)# 0*2 + 3*1 =3， 0*0 + 3*6 = 18# 1*2 + 4*1 =6， 1*0 + 4*6 = 24print(a.dot(b))print(np.dot(a,b))
2018-2-27 矩阵赋值
= 是浅拷贝，内存对象一致
view 内存对象不一致，但是数据值是相同的
copy 深拷贝，一个新的矩阵
import numpy as npa = np.arange(15).reshape(3,5)print (a)b = aprint (b is a)b[2,0] = 999print (b)print (a)c = a.view()print (c is a)c[2,1] = 888print (c)print (a)print (id(a))print (id(c))d = a.copy()print (d is a)d[2,2] = 777print (d)print (a)
2018-2-27 数据排序
import numpy as npdata = np.sin(np.arange(20)).reshape(5,4)print(data.shape[1])print(data.shape[0])print (data)ind = data.argmax(axis=0)print (ind)data_max = data[ind, range(data.shape[1])]print (data_max)b = np.sort(data, axis=1)print (b)print (data)data.sort()print (data)c = np.array([4,2,5,7])j = np.argsort(c)print (j)print (c[j])k = np.tile(c, (3,4))print (k)print (k[0,5])print (k.shape[1])print (k.shape[0])
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45[[ 0. 0.84147098 0.90929743 0.14112001] [-0.7568025 -0.95892427 -0.2794155 0.6569866 ] [ 0.98935825 0.41211849 -0.54402111 -0.99999021] [-0.53657292 0.42016704 0.99060736 0.65028784] [-0.28790332 -0.96139749 -0.75098725 0.14987721]][2 0 3 1][ 0.98935825 0.84147098 0.99060736 0.6569866 ][[ 0. 0.14112001 0.84147098 0.90929743] [-0.95892427 -0.7568025 -0.2794155 0.6569866 ] [-0.99999021 -0.54402111 0.41211849 0.98935825] [-0.53657292 0.42016704 0.65028784 0.99060736] [-0.96139749 -0.75098725 -0.28790332 0.14987721]][[ 0. 0.84147098 0.90929743 0.14112001] [-0.7568025 -0.95892427 -0.2794155 0.6569866 ] [ 0.98935825 0.41211849 -0.54402111 -0.99999021] [-0.53657292 0.42016704 0.99060736 0.65028784] [-0.28790332 -0.96139749 -0.75098725 0.14987721]][[ 0. 0.14112001 0.84147098 0.90929743] [-0.95892427 -0.7568025 -0.2794155 0.6569866 ] [-0.99999021 -0.54402111 0.41211849 0.98935825] [-0.53657292 0.42016704 0.65028784 0.99060736] [-0.96139749 -0.75098725 -0.28790332 0.14987721]][1 0 2 3][2 4 5 7][[4 2 5 7 4 2 5 7 4 2 5 7 4 2 5 7] [4 2 5 7 4 2 5 7 4 2 5 7 4 2 5 7] [4 2 5 7 4 2 5 7 4 2 5 7 4 2 5 7]]2163
2018-2-28 矩阵归一化
import numpy as npz = np.random.random((3,3))print (z)a , b = z.min(), z.max()print (a)print (b)a = np.array([1,2,3])x = z + aprint (x)#归一化y = (x - x.min())/(x.max() - x.min())print (y)
[[ 0.95219215 0.93771665 0.64158953] [ 0.48924913 0.14551862 0.33236475] [ 0.79020678 0.51215386 0.67116962]]0.1455186152370.952192153817[[ 1.95219215 2.93771665 3.64158953] [ 1.48924913 2.14551862 3.33236475] [ 1.79020678 2.51215386 3.67116962]][[ 0.21217227 0.66384982 0.9864431 ] [ 0. 0.30077608 0.84472172] [ 0.13793245 0.46880935 1. ]]
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