linear discriminant analysis python

provides a number of user-friendly and effective numerical routines such as numerical integration and optimization. The SCIPY provides modules for common tasks in optimization, linear algebra, integrals, and other data science.PandasPandas contains advanced data structures, as well as tools that make data analysis quick and easy. It is built on top of NumPy, making numpy-centric applications easier.1. Data

Kernel original link: Https://www.kaggle.com/pmarcelino/comprehensive-data-exploration-with-python The race is a return to the housing forecast. Prologue: Life is the most difficult to understand the ego. Kernel about four areas 1. Understanding the problem: in relation to the problem, study their significance and importance to each variable 2. Univariate Study: This competition is for target variables (projected house prices) 3. Multivariate

This article mainly records my understanding of several advanced syntax concepts: Anonymous functions, lambda expressions, closures, and decorators. These concepts are not specific to Python, but this article only describes them using Python. 1. anonymous functions Anonymous function is a function that is not bound to any identifier. it is used in the functional programming ages field. Typical applications:

Base LibraryA data Analysis library for Pandas:python (pip install pandas)Seaborn: Data visualization (pip install Seaborn)SCIPY: Numerical calculation library (pip install scipy) SciPy (pronounced "sigh Pie") is an open source mathematical, scientific, and engineering computing package. It is a convenient, easy-to-use, scientific and Engineered Python toolkit that includes statistics, optimization

C, C + +, FORTRAN code integration into Python tools Pandas: A large number of data structures and functions that handle structured data Precise indexing, reshaping, slicing, chopping, aggregating, selecting subsets High performance time series features and tools Matplotlib: The most popular library for plotting data graphsIpython: Enhanced Python Shell provides a robust and effi

: Pyplot,pylab (not recommended), object-oriented Adjustment of axes, addition of text annotations, area fills, and use of special graphics patches Financial students Note that: You can directly call the Yahoo Financial data mapping (real ... ） Scipy:A handy, easy-to-use Python toolkit designed for science and engineering. It includes statistics, optimization, integration, linear algebra module

on a linear stack (typically, for example, the C language). Because of the underlying implementations of these languages, if the function returns, the local variables defined in the function are destroyed as the function stack is recycled. However, closures require the non-local variable to be accessed at the bottom of the implementation to remain valid until the closure is executed, until the end of the closure's life cycle, which unexpectedly destr

functions of linear algebra, There are many functions about matrices in the linalg of numpy, and the same industry standard-level FORTRAN libraries are used with MATLAB and R.Random number generationThe Numpy.random module complements Python's built-in random, adding a number of functions to efficiently generate sample values for multiple probability distributions.#-*-encoding:utf-8-*-import NumPy as Npimport numpy.random as Nprfrom random import nor

SummaryNumPy is the basis that must be mastered in data analysis using Python. is the foundation package for high-performance Scientific computing and data analysis. By using numpy, we can perform fast standard mathematical function calculation without loop, and can do linear algebra, random number, Fourier transform a

[Machine Learning Algorithm Implementation] Principal Component Analysis (PCA)-based on python + numpy, pcanumpy[Machine Learning Algorithm Implementation] Principal Component Analysis (PCA)-based on python + numpy @ Author: wepon@ Blog: http://blog.csdn.net/u012162613/article/details/42177327 1. Introduction to PCA Al

1.PCA principlePrincipal component Analysis (Principal Component ANALYSIS,PCA) is a statistical method. An orthogonal transformation transforms a set of variables that may be related to a set of linearly unrelated variables, and the transformed set of variables is called the principal component.PCA algorithm:Implementation of the 2.PCAData set:64-D handwritten digital imagesCode:#Coding=utf-8ImportNumPy as

Using Python for data analysis--numpy basics: Arrays and Vector computing Ndarry, a multidimensional array with vector operations and complex broadcast capabilities for fast space-saving Standard mathematical function for fast operation of whole set of data without For-loop Tools for reading and writing disk data, and tools for manipulating memory-mapped files?

1. NumPyIn general, we will begin with a library of science as a list, and NumPy is one of the major repositories in the field. It is designed to handle large multidimensional arrays and matrices, and provides a number of advanced mathematical functions and methods that can be used to perform various operations.In the past year, the development team has made a number of improvements to the library. In addition to bug fixes and compatibility issues, key changes include style improvements, which a

1. NumPy: The realization of a variety of array object functions and Fourier transform and other scientific computing modules.2. SciPy: Provides more scientific computing functions, including matrices, solving linear equations, integral operations, optimization, etc.3. Matplotlib: A cross-platform numerical drawing package to draw high-quality 2d,3d images.4. mysql for python:python operation interface software package for MySQL database.5. PyQT: a qt

of data processing, the real significance of the project is that it (again) tells the story. 911 the nature of the event is negative, but there are many positive stories: Many heroes save a lot of people, community integration, and reconstruction.Unfortunately, the media environment in my thematic model is focused on negative energy, villains, and destruction. Of course, some of the individual heroes were praised in one or two articles, but none were wide enough to form a theme. On the other ha

has a mechanism called the Global Interpreter Lock, GIL, which prevents the interpreter from executing multiple Python byte-code directives at the same time. PS. Cython projects can integrate OpenMP (c framework for parallel computing) NumPy (numerical Python) Scientific Computing base package, function: Multidimensional array objects ndarray the array of elements to perform the element-level calculation

detected and we need to handle them. The general outlier processing methods can be broadly divided into the following types:• Delete records that contain outliers: Delete the records containing outliers directly;• Treated as missing values: treat outliers as missing values and process them using missing value processing methods;• Average correction: The outliers can be corrected with the average value of two observations before and after;• Do not process: data mining directly on data sets with

In the undergraduate stage, our common scientific calculation tool is MATLAB. Here is a very useful and powerful scientific computing library--numpy for Python. A powerful N-dimensional array object (a powerful N-dimensional array of objects) Sophisticated (broadcasting) functions (Advanced (broadcast? function Tools for integrating C + + and Fortran code (integrated c/C + + and FORTRAN tools) Useful

(file= ' d:/matlab/achivement2018-8-1.xlsx ')FS = Featureselection ()Fs.elasticnetfeatureselectplot (Df=rr.df_, l1_ratio=.08,Plot_width=16, Plot_height=8, Xlim_exp=[-2, 2], ylim=[-.1,.1])Fs.elasticnetrandomsearch (df=rr.df_)Fs.elasticnet_rs_bestFs.elasticnet (Rr.df_, alpha=.7, Normalize=true)Fs.elasticnet_coef_Fs.elasticnet_r2_Fs.eln.coef_Fs.featurebarhplot (FS.ELASTICNET_COEF_)Fs.elasticnet_coef_selected_Fs.randomforestrandomsearch (rr.df_)Fs.rf_rs_bestFs.randomforest (Rr.df_, n_estimators=139

I. Related NumPy(i) Official explanationsNumPy is the fundamental package for scientific computing with Python. It contains among other things: A powerful N-dimensional Array object Sophisticated (broadcasting) functions Tools for integrating C + + and Fortran code Useful linear algebra, Fourier transform, and random number capabilities Besides its obvious scientific uses, NumPy ca