python3.7安装numpy库_Python3 安装 numpy 科学库

[root@Singapore numpy]# wget https://pypi.python.org/packages/ee/66/7c2690141c520db08b6a6f852fa768f421b0b50683b7bbcd88ef51f33170/numpy-1.14.0.zip

[root@Singapore numpy]# md5sum numpy-1.14.0.zip

c12d4bf380ac925fcdc8a59ada6c3298 numpy-1.14.0.zip

[root@Singapore numpy]# unzip numpy-1.14.0.zip

[root@Singapore numpy]# cd numpy-1.14.0

[root@Singapore numpy-1.14.0]# cat INSTALL.rst.txt #安装说明

[root@Singapore numpy-1.14.0]# python3 setup.py build install --prefix /root/python/numpy #注意安装路径

[root@Singapore numpy-1.14.0]# echo "export PYTHONPATH=/root/python/numpy/lib/python3.6/site-packages" >> ~/.bashrc #注意安装路径

[root@Singapore numpy-1.14.0]# . ~/.bashrc

[root@Singapore numpy-1.14.0]# echo $?

0

[root@Singapore numpy-1.14.0]#

写一个线性回归 试一试[root@Singapore work.dir]# cat SimpleLineRegression.py

#!/usr/bin/python3

import numpy as np

def fitSLR(x,y):

n = len(x)

dinominator = 0

numerator = 0

for i in range(0, n):

numerator += (x[i] - np.mean(x)) * (y[i] - np.mean(y))

dinominator +=(x[i] - np.mean(x)) ** 2

print ("numerator:", numerator)

print ("dinominator", dinominator)

b1 = numerator/float(dinominator)

b0 = np.mean(y)/float(np.mean(x))

return b0, b1

def predict(x, b0, b1):

return b0 + x*b1

x = [1,3,2,1,3]

y = [14,24,18,17,27]

b0, b1 = fitSLR(x,y)

print ("intercept:", b0, " slope:", b1)

x_test = 6

y_test = predict(6, b0, b1)

print("y_test", y_test)

[root@Singapore work.dir]# ./SimpleLineRegression.py

numerator: 20.0

dinominator 4.0

intercept: 10.0 slope: 5.0

y_test 40.0

[root@Singapore work.dir]#