Python3 安裝 numpy 科學庫
阿新 • • 發佈:2018-01-11
cep pytho 科學 -- package ref setup.py .org numpy
[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 #安裝說明
[root@Singapore numpy-1.14.0]# python3 setup.py build install --prefix /root/python/numpy #註意安裝路徑
[root@Singapore numpy-1.14.0]# echo "export PYTHONPATH=/root/python/numpy/lib/python3.6/site-packages" >> ~/.bashrc #註意安裝路徑
[root@Singapore numpy-1.14.0]# . ~/.bashrc
[root@Singapore numpy-1.14.0]# echo $?
0
[root@Singapore numpy-1.14.0]#
寫一個線性回歸 試一試
[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]#
Python3 安裝 numpy 科學庫