Python3 Installing the NumPy Science Library

[[email protected] numpy]# wget https://pypi.python.org/packages/ee/66/7c2690141c520db08b6a6f852fa768f421b0b50683b7bbcd88ef51f33170/numpy-1.14.0.zip[[email protected] numpy]# md5sum numpy-1.14.0.zip c12d4bf380ac925fcdc8a59ada6c3298 numpy-1.14.0.zip[[email protected] numpy]# unzip numpy-1.14.0.zip [[email protected] numpy]# cd numpy-1.14.0[[email protected] numpy-1.14.0]# cat INSTALL.rst.txt #安装说明[[email protected] numpy-1.14.0]# python3 setup.py build install --prefix /root/python/numpy #注意安装路径[[email protected] numpy-1.14.0]# echo "export PYTHONPATH=/root/python/numpy/lib/python3.6/site-packages" >> ~/.bashrc #注意安装路径[[email protected] numpy-1.14.0]# . ~/.bashrc[[email protected] numpy-1.14.0]# echo $?0[[email protected] numpy-1.14.0]#

Write a linear regression test

[[email protected] work.dir]# cat SimpleLineRegression.py #!/usr/bin/python3import numpy as npdef 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, b1def predict(x, b0, b1):    return b0 + x*b1x = [1,3,2,1,3]y = [14,24,18,17,27]b0, b1 = fitSLR(x,y)print ("intercept:", b0, " slope:", b1)x_test = 6y_test = predict(6, b0, b1)print("y_test", y_test)[[email protected] work.dir]# ./SimpleLineRegression.py numerator: 20.0dinominator 4.0intercept: 10.0  

Python3 Installing the NumPy Science Library