# Numpy Vs Eigen

Separately, libraries such as NumPy, Torch, Eigen and Lush made array-based programming productive in general purpose languages such as Python, Lisp, C++ and Lua. 2 Chapter 10. Eigen is indeed LGPL3 licensed. Below Python packages are to be downloaded and installed to their default locations. The different chapters each correspond to a 1 to 2 hours course with increasing level of expertise, from beginner to expert. In this article, a few problems will be discussed that are related to face reconstruction and rudimentary face detection using eigenfaces (we are not going to discuss about more sophisticated face detection algorithms such as Voila-Jones or DeepFace). Converting between correlation and covariance matrices 4. In the Stanford NLP course cs224n's first assignment, and in the lecture video from Andrew Ng, they do singular value decomposition instead of eigenvector decomposition of covariance matrix, and Ng even says that SVD is numerically more stable than eigendecomposition. NumPy manual contents¶. diag(s) @ vh = (u * s) @ vh, where u and vh are 2D unitary arrays and s is a 1D array of a’s singular values. What do I need a numpy array for?’ Well, there are very significant advantages of using numpy arrays overs lists. Now, to find the eigen vectors, we simply put each eigen value into (1) and solve it by Gaussian elimination, that is, convert the augmented matrix (A – λI) = 0 to row echelon form and solve the linear system of equations thus obtained. NumPy Discussion - A mailing list devoted only to the NumPy package (not the SciPy stack). NumPy - http://www. InterfaceWithEigen. Thus, we sort the eigenvalues from highest to lowest and select the first k eigenvectors. The plotting module allows you to make 2-dimensional and 3-dimensional plots. This technique enhances our understanding of what principal components are and provides a robust computational framework that lets us compute them accurately for more datasets. Why is it useful? What does it not tell us? Is it useful?. Chocolatey integrates w/SCCM, Puppet, Chef, etc. Numpy was meant to be the lighweight/core version of things, and scipy was the full-fledged environment. Cholesky Decomposition in Python and NumPy. NumPy, which stands for Numerical Python, is a library consisting of multidimensional array objects and a collection of routines for processing those arrays. The situation here is not so simple. Next the eigen-analysis of the Kernel matrix is done. txt") f = load("data. In this case the above algorithm wont work because 1 + m00 + m11 + m22 = 0 which gives w=0 and so will cause division by zero. And numpy performs better (~26 seconds vs. > >It is licensed under the LGPLv3, so it is not compatible with the numpy license. txt") f = fromfile("data. Here is the changelog However, if it really is a bug, you could File a Bug. matplotlib will enable you to plot graphics. Next post => http likes 89. g % timeit eigen. The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation. Python vs FORTRAN. The roots of the characteristic equation are the eigen values of the matrix A. It will open a new window to select the compiler. The former is using numpy 1. The authors of that paper did a good job on reasons why computing Eigenvalue using Numpy might be unstable. txt") f = fromfile("data. In Matlab and R, the "chol" function gives the Cholesky decomposition. The line chart is based on worldwide web search for the past 12 months. This technique enhances our understanding of what principal components are and provides a robust computational framework that lets us compute them accurately for more datasets. NumPy also provides a function for calculating the standard deviation directly via the std() function. the eigen-decomposition of a covariance matrix and gives the least square estimate of the original data matrix. Eigenfaces This problem appeared as an assignment in the edX course Analytics for Computing (by Georgia Tech). This class has two primary purposes: A Tensor. This is a header lib that allows conversion between the C++ Eigen library and the Python Numpy librairy - vsamy/pygen-converter-pid. Delete elements, rows or columns from a Numpy Array by index positions using numpy. The 'Completely Painless Programmer's Guide to XYZ, RGB' was written in the hope that it might be of use to technically savvy people who know a whole lot about the code and the mathematics that goes into making an image editing program, but perhaps not so much about color spaces and ICC profiles. Categories Data Science, Python Basics Tags numpy tutorial, web class. Today, we've learned a bit how to use R (a programming language) to do very basic tasks. A silhouette close to 1 means the data points are in an appropriate cluster and a silhouette …. This looked a little suspicious to me. NumPy manual contents¶. matrix vs 2-D numpy. mean(X, axis = 0). Open3D: A Modern Library for 3D Data Processing Qian-Yi Zhou Jaesik Park Vladlen Koltun Intel Labs Abstract Open3D is an open-source library that supports rapid development of software that deals with 3D data. The line to run an octave script is octave. This is an header only library that can provide both Eigen global typedefs (e. using polar coordinates instead of cartesian. It is intended to simplify the process of writing C++ extensions using the Eigen library. The silhouette coefficient of a data measures how well data are assigned to its own cluster and how far they are from other clusters. Briefly, what LLVM does takes an intermediate representation of your code and compile that down to highly optimized machine code, as the code is running. You can vote up the examples you like or vote down the ones you don't like. eigh is? (Ditto for eigvals and eigvalsh. PCA starts with computing the covariance matrix. This section covers the use of Boolean masks to examine and manipulate values within NumPy arrays. The LAPACK library provides a high performance implementation of the Cholesky decomposition that can be accessed from Fortran, C and most languages. Differential equations are solved in Python with the Scipy. Tensor Contraction (HPTT) and Tensor Transposition (TCL) Hi all, I recently spent some time adding python interfaces to my tensor libraries: (19K) Download Attachment tcl_vs_numpy_vs_eigen. # This can be avoided by choosing a smaller random seed, e. using polar coordinates instead of cartesian. discriminant_analysis. NumPy Matrix and Linear Algebra Pandas with NumPy and Matplotlib Celluar Automata Batch gradient descent algorithm Longest Common Substring Algorithm Python Unit Test - TDD using unittest. NumPy is the foundation of the Python machine learning stack. For example, a padsize value of [2 3] means add two elements of padding along the first dimension and three elements of padding along the second dimension. Additionally, scipy. meshgrid(np Jul 20, 2016 For the kernel PCA, Gaussian Kernel is used to compute the distances between the datapoints and the Kernel matrix is computed (with the kernel trick), then normalized. ctypeslib) Datetime Support Functions. Use parallel programming. An eigenvector is a vector whose direction remains unchanged when a linear transformation is applied to it. At the time of writing of this blog, the latest version of OpenCV is 3. HDF5 9 Comments / Python , Scientific computing , Software development / By craig In a previous post, I described how Python's Pickle module is fast and convenient for storing all sorts of data on disk. Here is the Matlab script I used to…. You da real mvps! $1 per month helps!! :) https://www. Principal Component Analysis (PCA) is a simple yet popular and useful linear transformation technique that is used in numerous applications, such as stock market predictions, the analysis of gene expression data, and many more. Find the eigenvalues for the following matrix:. The set of eigen-. eig (a, b=None, left=False, right=True, overwrite_a=False, overwrite_b=False, check_finite=True, homogeneous_eigvals=False) [source] ¶ Solve an ordinary or generalized eigenvalue problem of a square matrix. After that, we’ll sort the eigen vectors in descending order based on their eigen values. The following are code examples for showing how to use scipy. Cheers, Benoit > > So, is there a bug in the eigen library or are the values intended to > be sorted like that (according to weight and direction/sign)? > > Thank you and best, > Susanne > > On Mon, Feb 21, 2011 at 10:20 AM, Robert Bocquier > wrote: >> Hi Benoit, >> >> I am interested in your answer to Susanne. Tensor Contraction (HPTT) and Tensor Transposition (TCL) Download Attachment tcl_vs_numpy_vs_eigen. PCA in numpy and sklearn produces different results. The element at ith row and jth column in X will be placed at jth row and ith column in X'. Use an int to make the randomness deterministic. Eigen Decomposition is one connection between a linear transformation and the covariance matrix. Singular Value Decomposition is a matrix factorization method utilized in many numerical applications of linear algebra such as PCA. PCA and Correspondence analysis in their relation to Biplot-- PCA in the context of some congeneric techniques, all based on. My intention was a more or less feature for feature and interface clone for NumPy. Java did not use array indexing like NumPy, Matlab and Fortran, but did better than NumPy and Matlab. com What is NumPy? Python is a fabulous language Easy to extend Great syntax - A free PowerPoint PPT presentation (displayed as a Flash slide show) on PowerShow. This way: exp. When a is higher-dimensional, SVD is applied in stacked mode as explained below. The OpenBLAS Haswell computation kernels were written in assembler. 0, iterated_power='auto', random_state=None) [source] ¶ Principal component analysis (PCA). Next post => http likes 89. Both Linear Discriminant Analysis (LDA) and PCA are linear transformation methods. Dear OpenCV users! 1 year after 3. I just care about 32 bit float matrix and I want to find the fastest tool to calculate eigen vectors and eigen values of a rectangular matrix. Second, the development of automatic differentiation  made it possible to fully automate the daunting labor of computing derivatives. Numpy VS SciPy. T) Now that we have the covariance matrix, we use the built-in numpy function to get the eigenvectors and eigenvalues. What is the best numerical library c/c++ ? I am using Matlab for my image processing research. Eigency is a Cython interface between Numpy arrays and Matrix/Array objects from the Eigen C++ library. A thorough understanding of the NumPy basics is an important part of any data scientist’s education. decomposition. Broadcasting rules apply, see the numpy. Using the C++ eigen library to calculate matrix inverse and determinant¶ Example showing how Eigen vectors and matrices can be passed in and out of C++ functions. A silhouette close to 1 means the data points are in an appropriate cluster and a silhouette …. Calculate the denominator for the covariance formula. Illustration, using the fact that the eigenvalues of a diagonal matrix are its diagonal elements, that multiplying a matrix on the left by an orthogonal matrix, Q. Eigen, compared to certain other C++ template libraries, is relatively easy on the compiler. You can vote up the examples you like or vote down the ones you don't like. NumPyのndarrayには、shapeという変数があります。このshapeはいたるところで使われる多次元配列の次元数を扱う属性です。本記事では、このshapeの使い方と読み方を解説します。. It is intended to simplify the process of writing C++ extensions using the Eigen library. com/patrickjmt !! Help me by being a mathematic. Further links. #include "NumCpp. CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model created by Nvidia. I am trying to prove some statements about singular value decomposition, but I am not sure what the difference between singular value and eigenvalue is. NumpyEigen makes it easy to transparently convert NumPy dense arrays and SciPy sparse matrices to Eigen with zero copy overhead while taking advantage of Eigen's expression template system for maximum performance. I am checking the new Version of Eigen, where they made some MSVC bug fixes. In this article we will discuss different techniques to get an element from vector by index or position. decomposition. py file under REQUIRED_PACKAGES. # This can be avoided by choosing a smaller random seed, e. Under the hood, the variable pi is gotten by default from the R base package, unless an other variable with the name pi was created in R’s. NDArray is created. Again, lets first see what we can do with just the change of basis. Interest over time of Eigen and Mission : Impossible (AutoDiff) Note: It is possible that some search terms could be used in multiple areas and that could skew some graphs. T) Now that we have the covariance matrix, we use the built-in numpy function to get the eigenvectors and eigenvalues. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions. A discussion on various ways to construct a matrix in R. It is designed to display arrays (Jagged and up to 4D) while debugging an application. eigenvector, while in$\textbf{y}A =\lambda \textbf{y}$,$\textbf{y}\$ is left eigenvector. model_selection import train_test_split from sklearn. Eigency is a Cython interface between Numpy arrays and Matrix/Array objects from the Eigen C++ library. Eigen3ToPython. Numpy contains nothing but array data type which performs the most basic operation like sorting, shaping, indexing, etc. Another advantage of using scipy. Briefly, what LLVM does takes an intermediate representation of your code and compile that down to highly optimized machine code, as the code is running. Bookmark the permalink. This is implemented using the _geev LAPACK routines which compute the eigenvalues and eigenvectors of general square arrays. tf is a package that lets the user keep track of multiple coordinate frames over time. However, for comparison, code without NumPy are also presented. The LAPACK library provides a high performance implementation of the Cholesky decomposition that can be accessed from Fortran, C and most languages. Matrix and Element-wise Operations. Sparse matrix support is only available for basic operations at this time. com/patrickjmt !! Thanks to all of you who supp. In this article we will discuss different ways to initialize a vector in C++. PCA yields the directions (principal components) that maximize the variance of the data, whereas LDA also aims to find the directions that maximize the separation (or discrimination) between different classes, which can be useful in pattern classification problem (PCA "ignores" class labels). Storing large Numpy arrays on disk: Python Pickle vs. dir/net/openpose_generated_bodyPartConnectorBase. • Hence all the evecs of a pd matrix are positive • A matrix is positive semi definite (psd) if λi >= 0. The main condition of matrix multiplication is that the number of columns of the 1st matrix must equal to the number of rows of the 2nd one. Creëer een CRAZY impact met je zielsmissie en ga het geld verdienen dat je bedoeld bent te maken!. matrix is matrix class that has a more convenient interface than numpy. linalg module are implemented in xtensor-blas, a separate package offering BLAS and LAPACK bindings, as well as a convenient interface replicating the linalg module. でとしてFourier変換を通じて与えられる． こうして、ハミルトニアンのq-表示の行列表現が与えられるので，数値的に対格化. Skip to content. linalg documentation for details. Computation of Eigenvectors. In particular, these are some of the core packages:. Simple Markov chain weather model. There are several ways to install CMake, depending on your platform. InterfaceWithEigen. Python Tools for Visual Studio: a rich IDE plugin for Visual Studio that supports CPython, IronPython, the IPython REPL, Debugging, Profiling, including running debugging MPI program on HPC clusters. The libMesh developers welcome contributions in the form of patches and bug reports (preferably with a minimal test case that reliably reproduces the error) to the official mailing lists, or via Github issues and pull requests. This is very helpful. And when we plot the generated eigen vectors from the co-variance we get something like below. Principal component analysis is a fast and flexible unsupervised method for dimensionality reduction in data, which we saw briefly in Introducing Scikit-Learn. It's harder to program then either python or C++ but is much more scalable. Hi, I'm one of Eigen's authors. After the relase of EPD 6. Deep Learning Tutorial - PCA and Whitening 03 Jun 2014 Principal Component Analysis. txt") f = fromfile("data. Ik label mezelf graag als een technische en wetenschappelijk genie. NumCpp: A Templatized Header Only C++ Implementation of the Python Numpy Library for use in C++ projects. gaussian37's blog. matmul Matrix multiply np. That's the whole essence of eigen values and vectors within PCA. Although the actually code is quite simple, it is the configuration issues such as importing right library, drivers to use, how to access them, and so forth, that make it an uphill battle. I will not go through the theoretical foundations of the method in this post. They are from open source Python projects. 2 Chapter 10. The formula for the length of a 2D vector is the Pythagorean Formula. Eigen MKL eigen库 Intel-MKL Intel MKL vs vs. In case of C interface the approximated curve is stored in the memory storage and pointer to it is returned. a search for solutions processor_cpu precision_double precision_single computation_synch eigen_real eigen_complex scaling_manual scaling_auto scaling_always. CMake is an open-source, cross-platform family of tools designed to build, test and package software. I think >> the license would allow to include it in numpy directly. The following are code examples for showing how to use numpy. Some of the operations covered by this tutorial may be useful for other kinds of multidimensional array processing than image processing. Business Day Functions. ndarrays can be created in a variety of ways, include empty arrays and zero filled arrays. Creating ndarrays. linalg is that it is always compiled with BLAS/LAPACK support, while for NumPy this is optional. And hopefully with a much easier to use interface as well. meshgrid(np Jul 20, 2016 For the kernel PCA, Gaussian Kernel is used to compute the distances between the datapoints and the Kernel matrix is computed (with the kernel trick), then normalized. In a nutshell, given the singular decomposition of a matrix A, the Moore-Penrose pseudoinverse is given by. Closer to data science, I will discuss about popular Python libraries like NumPy, Pandas, SciPy, Matplotlib, Scikit-Learn and NLTK. PCA yields the directions (principal components) that maximize the variance of the data, whereas LDA also aims to find the directions that maximize the separation (or discrimination) between different classes, which can be useful in pattern classification problem (PCA "ignores" class labels). Every function is contained in a unique Python function. There are different kinds of datatypes provided by NumPy for different applications but we'll mostly be working with the default integer type numpy. The situation here is not so simple. Determinant is a very useful value in linear algebra. to create overloads for different precisions and complex vs real-valued matrices. 2 with Numpy-MKL 1. Previous Page. TensorFlow is a Python library for high-performance numerical calculations that allows users to create sophisticated deep learning and machine learning applications. The different chapters each correspond to a 1 to 2 hours course with increasing level of expertise, from beginner to expert. And numpy performs better (~26 seconds v. Tutorials on the scientific Python ecosystem: a quick introduction to central tools and techniques. linalg module performs Cholesky decomposition. The Visualization Toolkit (VTK) is open source software for manipulating and displaying scientific data. This transformer performs linear dimensionality reduction by means of truncated singular value decomposition (SVD). visual studio 2015 下载安装汉化 参考 【VS2015+OpenCV】安装配置 - 简书. MatrixXd, Vector3i, ArrayXXf, …) and user-defined typedefs. Skip to content. The Open3D frontend exposes a set of carefully selected data structures and algorithms in both C++ and Python. import numpy as np. nl account of bent u abonnee van Het Financieele Dagblad?. [ 2%] Building NVCC (Device) object src/openpose/CMakeFiles/openpose. And when we plot the generated eigen vectors from the co-variance we get something like below. While NumPy is not the focus of this book, it will show up frequently throughout the following chapters. Then for transverse modes: "q dot eigen_i. table("data. Next post => http likes 89. Singular Value Decomposition. stubbornhuang. Thanks to all of you who support me on Patreon. Any value of λ for which this equation has a solution is known as an eigenvalue of the matrix A. By selecting different configuration options, the tool in the PyTorch site shows you the required and the latest wheel for your host platform. TruncatedSVD (n_components=2, algorithm='randomized', n_iter=5, random_state=None, tol=0. Calculator of eigenvalues and eigenvectors Matrix calculator العَرَبِيَّة Български Català Čeština Deutsch English Español فارسی Français Galego Italiano 日本語 ‪한국어‬ Македонски Nederlands Norsk Polski Português Română Русский Slovenčina Türkçe Українська اردو. I think >> the license would allow to include it in numpy directly. Eigen is small, so it is feasible to include a copy of it in your own source tree, if you want to. An eigenvector is a vector whose direction remains unchanged when a linear transformation is applied to it. 8线程不同矩阵操作 该网友得出如下结论. Asynchronous. If you want to add (or subtract) two matrices, their dimensions must be exactly the same. In mathematics, power iteration (also known as the power method) is an eigenvalue algorithm: given a diagonalizable matrix, the algorithm will produce a number , which is the greatest (in absolute value) eigenvalue of , and a nonzero vector , which is a corresponding eigenvector of , that is, =. I mean, how else do you think the algorithm got its name? numpy: This module R vs Python - Which is best? Whether you have experience in other coding tools or not, the individual features of these two, including the vast arrays of libraries and. We must have. linalg documentation for details. matplotlib will enable you to plot graphics. A Computer Science portal for geeks. (转)NumPy 多维数组 （转）Numpy学习——数组填充np. I mean, how else do you think the algorithm got its name? numpy: This module R vs Python - Which is best? Whether you have experience in other coding tools or not, the individual features of these two, including the vast arrays of libraries and. So Pandas is not an alternative to Numpy. In this article, I will give you a quick introduction in how to get started with Armadillo, a C++ Matlab like Linear Algebra Library on Windows, Mac and Linux. An example of using ODEINT is with the following differential equation with parameter k=0. Eigen vs Numpy时间对比有人说Eigen太慢了,用Numpy好一点,我们来看一看是不是这样的Eigen C++ 程序 t_time. Numba generates specialized code for different array data types and layouts to optimize performance. 4 with the MKL. NumPy allows for efficient operations on the data structures often used in machine learning: vectors, matrices, and tensors. If you struggle with the NumPy library — fear not!. What is NumPy? Why is NumPy Fast? Who Else Uses NumPy?. in python opencv+numpy is very practical. I read in this question that eigen has very good performance. Building NumPy and SciPy with Intel MKL; No Cost Options for Intel® Parallel Studio XE, Support Yourself, Royalty-Free. The details of the technique can be found here. The characteristic polynomial of. “PyTorch - Basic operations” Feb 9, 2018. In this example. array([[1,2],[3,4]]) #Passing the values to the eig function l, v = linalg. This is a header lib that allows conversion between the C++ Eigen library and the Python Numpy librairy - vsamy/pygen-converter-pid. busdaycalendar. Pandas Pandas is built on top of NumPy. table("data. R vs Python – Which is best? Whether you have experience in other coding tools or not, the individual features of these two, including the vast arrays of libraries and packages may initially seem daunting, but don't worry, we're here to help!. It will open a new window to select the compiler. #include "NumCpp. Downloading Libigl¶. Is covariance of more than two variables possible? be used to decompose the matrix into its eigenvalues and eigen-vectors. pdf (155K) Download Attachment. linalg is that it is always compiled with BLAS/LAPACK support, while for NumPy this is optional. bashrc (for example), but it's not clear how to do this. You are expected to become familiar with one of Matlab/Octave or Python, and use it to check your understanding through the course, and for the assessed. Computation of Eigenvectors. Scientific and Engineering Computing, Numpy NDArray implementation and some working case studies are reported. This introductory tutorial does a great job of outlining the most common Numpy array creation and manipulation functionality. Boosting numpy: Why BLAS Matters I recently noticed that the same code on the same machine had vastly different run times in different virtual environments. NumPy: Beginner's Guide - Third Edition [Ivan Idris] on Amazon. If you don’t need OpenCV image stuff, it is better using Eigen. We will also share C++ and Python code written using OpenCV to explain the concept. It seems to lie in the semantics or datatypes. でとしてFourier変換を通じて与えられる． こうして、ハミルトニアンのq-表示の行列表現が与えられるので，数値的に対格化. Again, lets first see what we can do with just the change of basis. linalg over numpy. And for Numpy / Scipy users please take note of the pdf that I have linked above. Chocolatey integrates w/SCCM, Puppet, Chef, etc. Number of eigen vectors to use for the spectral embedding. lobpcg (Locally Optimal Block Preconditioned Conjugate Gradient Method) * works very well in combination with PyAMG * example by Nathan Bell: """. ROS is an open-source, meta-operating system for your robot. Эффект Eigen Matrix vs Numpy Array. This is a simple example on how to use boost. Transpose of a matrix is a task we all can perform very easily in python (Using a nested loop). Vectors are used throughout the field of machine learning in the description of algorithms and processes such as the target variable (y) when training an algorithm. Eigen, compared to certain other C++ template libraries, is relatively easy on the compiler. Ask Question Asked 5 years, 9 (in my humble opinion) a happy medium. import numpy as np import eigen A = np. You can vote up the examples you like or vote down the ones you don't like. Please note that this is not an issue; if is an eigenvector of a matrix , we have. Reply | Threaded. For example, at the time when a parameter is created in a Block, an mxnet. But others that say things like. When we construct a matrix directly with data elements, the matrix content is filled along the column orientation by default. Getting started with Latent Dirichlet Allocation in Python. TestCase class Simple tool - Google page ranking by keywords Google App Hello World Google App webapp2 and WSGI Uploading Google App Hello World Python 2 vs. Matrix and Element-wise Operations. The array (here v) contains the corresponding eigenvectors, one eigenvector per column. In numpy, this operation would be implemented as: X -= np. RGB) image. Theano • Both use static graph declarations • Faster compile times compared to Theano • Streamlined saving/restoration in TensorFlow • Data/Model parallelism across multiple devices is easier with TensorFlow. Linear algebra¶. The eigenvectors are normalized so their Euclidean norms are 1. Vectors are used throughout the field of machine learning in the description of algorithms and processes such as the target variable (y) when training an algorithm. Business Day Functions. Linear Algebra Shootout: NumPy vs. Released as open source software in 2015, TensorFlow has seen tremendous growth and…. Eigenvectors in Matlab vs. 5 >#points：16,777,216(8 ** 3 * 32 ** 3) > Xrange时间：1487. LinearDiscriminantAnalysis¶ class sklearn. By voting up you can indicate which examples are most useful and appropriate. September 2018; August 2018; July 2018; April 2018; March 2018; February 2018; January 2018; December 2017; November 2017; October 2017; September 2017; August 2017; July 2017; June 2017; May 2017. python already installed):. Using numpy arrays as function arguments and return values; Using the C++ eigen library to calculate matrix inverse and Computational Statistics in Python. print a sparse matrix with Eigen. They are not better, but that could make a good >> default implementation for numpy when their is no blas installed. NDArray is created. If you don't need OpenCV image stuff, it is better using Eigen. 1 Introduction to Eigenvalues Linear equationsAx D bcomefrom steady stateproblems. 0 or later and have run using LinearAlgebra, Statistics, Compat. This chapter enters a. Cheers, Benoit > > So, is there a bug in the eigen library or are the values intended to > be sorted like that (according to weight and direction/sign)? > > Thank you and best, > Susanne > > On Mon, Feb 21, 2011 at 10:20 AM, Robert Bocquier > wrote: >> Hi Benoit, >> >> I am interested in your answer to Susanne. The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation. I did some benchmark with python2 came with anaconda distribution. NDArray should be created.