vectorization python

vectorization functions (let's call that).Here are a few examples:#-*-encoding:utf-8-*-import NumPy as Npimport numpy.random as Nprimport pandas as pd# a function that receives two arrays, the corresponding value takes the maximum value x = Npr.randn (8) y = Npr.randn (8) #注意不是max函数z = np.maximum (x, y) print x,y,z# Although not common, some Ufunc functions can indeed return multiple arrays. The MODF function is an example that separates the integer

first statistical sample, J of the sample J property; A For step)The formula will always be iterated until a stop condition is reached (for example, the number of iterations reaches a specified value or the algorithm achieves an allowable error range).2, to quantify vectorization solutionVectorization is the use of matrix calculations instead of a for loop to simplify the calculation process and improve efficiency. As above, Σ (...) is a summation pr

"Python Machine learning and practice – from scratch to the road to Kaggle race" very basicThe main introduction of Scikit-learn, incidentally introduced pandas, NumPy, Matplotlib, scipy.The code of this book is based on python2.x. But most can adapt to python3.5.x by modifying print ().The provided code uses Jupyter Notebook by default, and it is recommended to install ANACONDA3.The best is to https://www.kaggle.com registered account, run the fourth

intermediate tool.3, the language is simple to get started quickly, do not need to explicitly define the variable type. For example, the following simple three lines of code, you can define a unary linear regression, is not very cool:X Y Fit At the same time, the R language has a high degree of support to vectorization, and it is an implementation of high parallel computing and avoids the use of many cyclic structures by

, array operations Arrays can perform batch arithmetic operations on each element without looping, a process called vectorization, which replaces loops with array expressions. The vectorization array performs one or two data levels faster than the pure Python method. Any arithmetic operations between two arrays of equal size will apply their operations to the op

NumPy is the foundation of many of the science packages in Python. It provides a special data type, Ndarray, which is optimized for vector computing. This object is the core of most algorithms in scientific numerical computation. Compared to native Python, the numpy array can be used to achieve significant performance acceleration, especially if your calculations follow a single instruction multi-data flow

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Baptism soul, practice python (2) -- python installation and configuration, python -- python Install python and basic configurations: Python Official Website: www.python.org Open the website and download the corresponding version

Python learning notes-python program running, python-python I am a beginner in python and write down some of my ideas. Please ignore it. Install the python editor and configure the environment (see install and configure

#equivalent to 3x^2+4x+5Printp+PPrintP*p#equivalent to 9x^4+24x^3+46x^2+40x+25PrintP ([A])Print "indefinite integral of P (x), specifying a constant of 2"PrintP.integ (k=2) Print "first-order derivative of P (x)"PrintP.deriv (1)#1-order derivative is expressedPrintP.deriv (2)#2-order derivative is expressedPrint "********scipy function vectorization ********"ImportNumPy as NPdefCompare (A, b):ifA>B:returnA-bElse: returnA +bPrintCompare (10, 3)P

. Net programmer Python path --- Python basics, python --- python Recently, I am curious about dynamic languages. So I chose the Python language. 　　I. Python basics: 1. print outputs data to P