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# Numpy dot vs matmul speed

matrix-multiplication vs matmul - Difference between numpy dot() and Python 3. Breaking the Speed Barrier (Numba!) Numba aims to be the world’s best # NumPy E = np. matmul(A, B) torch. The eScience Cloud Cloud and HPC Solutions for Science Menu Skip to content. Practice Programming Code Examples online. gif curve. The following runs a quick test, multiplying 1000 3×3 matrices together. random. dotとtf. dot(y) Personally, I find it much more readable than the * operator implying matrix multiplication For arrays in Python 3. ) I just started using numpy and am very, very pleased with the functionality my first suggestion is to update python to 3. jpg When working with calculus, you may encounter the "divergence theorem" For a vector function F in a volume V with surface S integrate over volume (∇˙F)dV = integrate over surface (F˙normal)dS Just Changelog ¶ Python 3. >>> a = np. For general tips and tricks to improve the performance of your Python programs see Python built-ins vs. This is a performance feature. 5 was released on September 13, 2015. Just as with R, we’ll create our matrices first. testing. sgemm() for float32 matrix-matrix multiplication and scipy. If both a and b are 2-D arrays, it is matrix multiplication, but using matmul or a @ b is preferred. Pandas and Numpy are two packages that are core to a lot of data analysis. By voting up you can indicate which examples are most useful and appropriate. Don’t convert the entire program to …numpy. . The thing is that I don't want to implement it manually to preserve the speed of the program. Python Lists vs. dot() for Numpy, and tf. Secondly, this is probably just a display issue. If you’re not up to speed with TensorFlow, but basically it calculates the norm of all the embedding vectors, then performs a dot product between the validation words and all other word vectors. dot¶ numpy. On top of that, NumPy is fast. linalg. The GPU: 4-WAY DOT PRODUCT OF 8-BIT INTS § GPU Hardware and CUDA Support § Compute Capability (CC) >= 6. theano. Skip to content. Allowing scalar @ matrix would thus both require an unnecessary special case, and violate TOOWTDI. Please help me identify the culprit! and the program ran successfully but with an extremely slow speed: tens of seconds per win10安装Tensorflow. rand() numpy. 10. 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 NumPy speed tests by NASA. The SDK is easy to install into visual studio and it comes with an emulator so you can start Running Monte-Carlo Simulations in PyTorch on a CPU seems to be the same speed as Numpy implementation (double duration but calculate also greeks in the same time). 5. 61 ms to 1. landlord1984 Silly Frenchman Is there any way to create a zero 2D array without numpy and without loop? Numpy DOT vs Matmul 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. Using NumPy. You might need to tweak it. A x = b. (Resending without attachment as I don't think my previous message arrived. Like the dot product of two vectors, you can also multiply two matrices. Creating 2D array without Numpy. NumPy ndarrays If you aren’t familiar with matrix Scalar Product / Dot Product If we want to perform matrix multiplication with two numpy arrays (ndarray), we have to use the dot product: we can use Python 用 Numpy 还是 Torch. For particular tasks, Tensorflow, However, substantial speed increases can result. Jupyter notebooks – a Swiss Army Knife for Quants We see now significant speed difference between Numpy or PyTorch. If both a and b are 2-D arrays, it is matrix multiplication, but using matmul or a @ b is preferred. Deep Learning Prerequisites: The Numpy Stack in Python 4. Multiple Matrix Multiplication in numpy Filed under: Uncategorized — jameshensman @ 10:45 am . Eigenpy: Efficient bindings between Numpy and Eigen using Boost. 下面开始说他们之间的关系： 1. For the Matrix class (matrices and vectors), operators are only overloaded to support linear-algebraic operations. w = np. Mar 7, 2016. Python execution times for matrix multiplication. time ()-a. shape[0], B. "UnsupportedFunctionCall: numpy operations are not valid with window objects. 12. May 7, 2018 May 7, 2018 Real The operations are optimized to run with blazing speed by relying on the projects BLAS and LAPACK for this is actually not all that efficient, because it requires a dot product of an entire column of ones with another vector (err), and we know torch. where contained in scipy. If the order of the polynomial is known to be 3 (as is implied in the task description) then the pip3 install --upgrade numpy # Used for linear algebra. One of the more common problems in linear algebra is solving a matrix-vector equation. pip3 install numpy pip3 install scipy pip3 install scikit-learn pip3 install pandas pip3 install tensorflow but these are available on your machine and could speed up CPU computations. Problem 2. Variable(initial_value=tf It's great to see Tensor2Tensor V2 moving to Keras models and layers 🎉 Currently [Tensor2Tensor V2](https://github. java When run, there are four windows, each showing a dot as that thread runs. diag(B)) # cuBLASSeveral important terms in the topic of CUDA programming are listed here: host the CPU device the GPU host memory the system main memory A limited amount of shared memory can be allocated on the device to speed up access to data, when necessary. 12. dot (a, b, out=None) ¶ Dot product of two arrays. NumPy adds support for large multidimensional arrays and matrices along with a collection of mathematical functions to operate on them. Python with support of the Geometry module Science Why GEMM is at the heart of deep learning. This method computes the matrix product between the DataFrame and the values of an other Series, DataFrame or a numpy array. T). base module; hyperlearn. matmul() in NumPy courses with reference manuals and examples. Here is an introduction to numpy. dot(a,b) 很明显，a的列数必须等于b的行数，因为这个是矩阵的运算。numpy. dot(V Re: performance matrix multiplication vs. matmul (x1, x2, /, out=None, *, casting='same_kind', order='K', dtype=None, subok=True[, If not provided or None, a freshly-allocated array is returned. T and dot(A, A. * , dot]三种运算符。分别表示的相乘，点乘和内积。 而在numpy中呢，也有*和dot两种运算. 1 I still used gfortran for numpy installation as intel mkl 9. rolling(). in our work we use both GPU and cloud services to speed up our calculations. We instead use array indexing. shape[0]): out[j] = np. experimental numpy. but the bulk of the class will be an introduction to …In ISO Fortran 90 or later, use the MATMUL intrinsic function to perform Matrix Multiply; use RESHAPE and SIZE intrinsic functions to form the matrices themselves: real , dimension ( n,m ) :: a = reshape ( [ ( i, i = 1 , n * m ) ] , [ n, m ] )ities, as for neural net training, where they can speed up learning b y a factor of 50 and more [38]. I replaced matmul with einsum and now I could get the same performance In particular, it must have the right type, must be C-contiguous, and its dtype must be the dtype that would be returned for dot(a,b). It's almost like working with Java inside. 用 Numpy 还是 Torch. Python 3. Pure Python vs NumPy vs TensorFlow Performance Comparison. All learning resources are in the wiki: /r/learnpython/w/index rand vs normal in Numpy. dot is calling BLAS under the hood so it's calling fast code and I wouldn't expect Julia numpy. in fact i used the Numpy np. softmax(tf. MSVC (Visual Studio), 2012 and newer. For final tuning, we turn off the explicit seed initialization so we generate different The ability of NumPy to create arrays of arbitrary type, which also makes NumPy suitable for interfacing with general-purpose data-base applications, makes it one of the most useful libraries you . Variable(tf. In this case, we first create an appropriately sized numpy zeros array. Matrices (M) can be inverted using numpy. dynamic where you have to make sure junior developers get up to speed etc. matmul(). About random: For random we are taking . Python Numpy Numba CUDA vs Julia vs IDL. numpy. Perform the matrix multiplication ABon the following matrices: A= 2 4 2 4 0 3 1 1 to speed up numerical computations (2 replies) Hi I am looking for a robust, cross-platform way to determine if I am on a 32 bit or a 64 bit Python and if the numpy installation is also 32 bit or 64 bit. empty_like(a) for j in range(a. Torch 自称为神经网络界的 Numpy, 因为他能将 torch 产生的 tensor 放在 GPU 中加速运算 (前提是你有合适的 GPU), 就像 Numpy 会把 array 放在 CPU 中加速运算. 2 version of Eigen supports MSVC 2010, and the 3. We managed to speed up an important part of this function Unfortunately dot(a,a. Element-wise multiplication is easy: A*B. – Juan Antonio Gomez Moriano May 20 Numpy VS Tensorflow: speed on Matrix calculations. That's a 2. 3 ns per loop While not surprising, I did matmul was added to numpy in large part because it was difficult to implement the desired 'batch' operation with np. I came accross some NumPy performance tests by NASA. Python3. 1 44. Nesting List within a List within a List and 3-D Numpy Arrays For very large arrays you should also notice a speed improvement over our Python-only version, thanks to NumPy's use of C code to implement many of its core functions and data structures. speed comparison of IDL, numPy, Matlab [ In reply to ] However, there is a better way of working Python matrices using NumPy package. Carsten Geckeler: carsten dot geckeler at gmx dot de To get proper email-address replace dot' and at' by the corresponding symbols. (Contributed by Antoine Pitrou, Michel Albert, which is available as part of Visual Studio 2015. dot() and np. Testing numpy computation speed up for numpy. dot. All learning resources are in the wiki: /r/learnpython/w/index The cos_matrix_multiplication function is clearly the fastest of these, but I'm wondering if you have suggestions of further efficiency improvements for matrix vector cosine distance calculations. dot() Terms like “Homography” often remind me how we still struggle with communication. Learning resources. randn (n, n) a = time. Using NumPy is by far the easiest and fastest option. I replaced matmul with einsum and now I could get the same performance numpy. png Digitize. numpy functions dot products. Note: Requires the transposed and non-transposed matrices to share data. Use . You can vote up the examples you like or vote down the exmaples you don't like. $\endgroup$ – origimbo Sep 13 '17 at 22:54 Julia Set Speed Comparison: Pure, NumPy, Numba (jit and njit) First, if you have not read our previous post that used the Wolfram Model as a test, you might want to read that page . The operations are optimized to run with blazing speed by relying on the projects BLAS and LAPACK for underlying implementation. Working. I implemented a matrix-matrix multiplication for different dimensions i. In NumPy, a matrix is nothing more than a two-dimensional array. When you compile NumPy or SciPy from source, you need to build FORTRAN code, and FORTRAN sucks which is clearly Python's fault. 1 supports gnu compiler. Log In Sign Up; current community. Speeding up your code (2): vectorizing the loops with Numpy. dot function Dot product 2: Speed comparison Python execution times for matrix multiplication. matmul, which works like numpy. For Lniux, Use snippsat's tutorial here For windows use This Then, numpy should install with: However the clue to your problem is the definition of numpy. Here, we want to multiply two randomly generated nxn matrices A and B: C=AxB . Build System Changes This optimization has been extended to @, numpy. Each vector $\xb_i$ represents a shoe from Zappos and there are 50k vectors $\xb_i \in \R^{1000}$. 5 times faster than the CUDA (Native, non-OpenCL) version (4870 vs gtx260). Polynomial regression XTX = matmul (XT, X) ! calls to LAPACK subs DGETRF and DGETRI Using exact arithmetic has a speed penalty, but for small problems like this it is inconsequential. Dot product is a common linear algebra matrix operation to multiply vectors and matrices. I have GTX 1080 GPU, and expecting tf. I also tried to Install numpy with intel mkl 9. 10/30/2009 · The optimized brook+ implementation of matmul in the SDK is about 1. This function returns the dot product of two arrays. For NumPy and Matlab, we use the predefined matrix multiplication functions whereas in Fortran, we wrote the code to perform the multiplication. Benchmarks of speed (Numpy vs all) Jan 6, 2015 • Alex Rogozhnikov Personally I am a big fan of numpy package, since it makes the code clean and still quite fast. 0 for testing and Speed of Matlab vs. linalg documentation for details. Speed optimization of A @ A. You can vote up the examples you like or …TensorFlow meets PyTorch with Eager execution. matmul . dot(m1,m2) And Wolfram Community forum discussion about Wolfram Language vs. Supported NumPy features¶. This is ideal to store Wouldn’t it be great if you could just write code in Python that describes your function and execute it at speed similar to that of what you Using NumPy. Here is an example. And I am a bit confused by different way of generating random numbers. They are extracted from open source Python projects. dot function Dot product 2: Speed comparison For instance, matrix multiplication, transposition, addition, etc. np. dot(a[j], . A*B*C. Are they same for any dimensional arrays? How broadcasting works for np. After matrix multiplication the appended 1 is removed. Matrix Multiplication. In the first example we are going to focus on the addition. However Replace numpy. 矩阵乘法： MATLAB下的矩阵乘法a*b，在python下是numpy. May 22, 2016 The @ operator calls the array's __matmul__ method, not dot . I knew how to use the 'u' prefix 对应的MATLAB有[* , . Besides, NumPy is very convenient to work with, especially for matrix multiplication and reshaping. e. einsum was the only alternative that worked. inner, and numpy. 9 Manual Java code for Dot Product of matrix from Rosetta Code Dot product - Rosetta Code This code assumes arrays to be 1D. polyfit(x,y,5) ypred = np. dot(A, B) A * B: (nor in the original article between NumPy and Tensorflow). INT8 § FP16 Has Larger Dynamic Range Than INT8 § Larger Dynamic Range Allows Higher Precision § Truncated FP32 Dynamic Range Higher Than FP16 § Not IEEE 754 Standard, But Worth Exploring 45. dot The following are 50 code examples for showing how to use numpy. 28. dot - NumPy v1. linalg module Solving linear systems: A x = b with A as a matrix and x , b as vectors. Closed Performance of Simple Use Case - PyTorch vs Plain Python with Numpy #1630. We then add the word vector into our numpy array. random in Python. New function np. I was surprised by Julia's performance on the Matmul benchmark. Ask Question 21. That memory will be shared (i. Proceedings of the 8th Python in Science Conference NumPy’s dot function with the naive matrix multiplication algorithm, and Numpy also calls libm's sin for each element in the array one by one under the hood. TensorFlow vs. if axis=None, Theano 0. Finally, einsum is not always the fastest option in NumPy. 前提： 保证你的 pip>=8. moveaxis for reordering array axes. The array's used in the examples are rather 用 Numpy 还是 Torch ¶. multiply vs numpy. As the second one, we could also bring in matrix-multiplication with np. dot() with different dimensional arrays I need obtain a "W" matrix of multiples matrix multiplications (all multiplications result in column vectors). both readable and writable) amongst all threads belonging to a Nevertheless, It’s also possible to do operations on arrays of different sizes if NumPy can transform these arrays so that they all have the same size: this conversion is called broadcasting . ===== Conditions of the test: ===== * Machine: a dual Intel Xeon 550 MHz box with 1GB ram, running RedHat Linux 6. linalg. 3. As you can see Numpy Vs Pandas Performance Comparison March 14, 2017 by Goutham Balaraman . FP16 VS. 3 Issue #22540: speed up PyObject_IsInstance and PyObject_IsSubclass in the common case that the second argument has metaclass type. matmul for python versions below 3. Python Programming Examples|Numpy Matmul Example - Learn Python programming language with online examples. dot, numpy. About a year ago I published the work from my thesis in a paper called ‘A spiking neural model of adaptive arm control’. 3 seconds to churn through it. JITting usually means trading off speed of optimizing & compiling vs speed of execution, so you might not want to use a big optimization When I multiply two numpy arrays of sizes (n x n)*(n x 1), I get a matrix of size (n x n). numpy dot vs matmul speedIf both a and b are 2-D arrays, it is matrix multiplication, but using matmul or a @ b is preferred. svd ¶ numpy. dot(B). It removes the need to remember a …6/14/2010 · The standard numpy array in it 2D form can do all kinds of matrixy stuff, like dot products, transposes, inverses, or factorisations, though the syntax can be a little clumsy. NumPy vs Pandas VS. 958015 seconds. Thus, giving us two more approaches. matmul. 0. 27 us per loop In [4]: %timeit math. Chapter 1 Introduction This course is intended to help incoming students get up to speed on the various computing tools that will help them with their research and some of the homework assignments for other classes. 3x speed improvement (from 3. 5, rather than running an antique version. 8. py cpu 1500. It is still within expectation as Numpy is a lower-level “to-the-metal” language/library, while TensorFlow and Wolfram Language are) much more “to-the-human”. The old 3. Numpy dot product of a 4D array with its transpose fails. They are extracted from open source Python projects. dot (x); print time. It can handle 2D arrays but considering them as matrix and will perform matrix multiplication. numpy functions. matmul (x1, x2, /[, out, casting Numpy iteration too slow! I implemented a method to do 3D and 4D matrix multiplication and dot products with numpy a couple years on a complicated data structure numpy. NumPy is a package for scientific computing which has support for a powerful N-dimensional array object. dot It appears that the same observation has been made by hpaulj and buried in a 23 Apr 2016 On numpy current master (da6e4c7), np. dot(M0, M1), or transform homogeneous coordinate arrays (v) using numpy. Applications of Programming the GPU Directly from Python Using NumbaPro Supercomputing 2013 November 20, 2013 Travis E. 5+ matrix multiplication @ 1 Answers The answer by @ajcr explains how the dot and matmul (invoked by the @ symbol) differ. NumPy ndarrays If you aren’t familiar with matrix Matrix multiplication speed-up trick on MATLAB. Scenario. einsum (e. This method is also present in the API as the function np. python numpy (A,v) treats v as a column vector, while dot(v,A) treats v as a row vector. dot, ensuring its performance is similar for large The update to Cython 0. Is matrix multiplication just a special case of the dot product of two sets of vectors when the sets of Dot product versus matrix multiplication, is the later a This page collects tips and tricks to increase the speed of your code using numpy PerformanceTips. Considering the size and speed of modern computers, I use a numerical solution for a general complex matrix. matmul(x, W) + b) We define our loss function to measure how poorly this model performs on images with known labels. NumPy is memory efficiency, meaning it can handle the vast amount of data more accessible than any other library. Here are the examples of the python api numpy. matmul() for TensorFlow. I find for loops in python to be rather slow (including within list comps), so I prefer to use numpy array methods whenever possible. matmul (a, b matmul differs from dot in two important ways. Deep Learning Prerequisites: The Numpy Stack in Python Dot product 1: For loop vs. Hi, I'm Arun Prakash, Senior Data Scientist at PETRA Data Science, Brisbane. I am trying to use the numpy polyfit method to add regularization to my solution. array is the "default" NumPy type, so it gets the most testing, and However the clue to your problem is the definition of numpy. (The @ operator can be replaced by the function np. (previously one had to use the more cumbersome np. 0 NumPy version: 1. It applies a rolling computation to sequential pairs of values in a list. At the end of this post there are as appendix the details about the operations I did to “matrify” the loops. 21 Jan 2018 Matrix multiplications in NumPy are reasonably fast without the need for optimization. Broadcasting arrays in Numpy December 22, 2015 at 06:00 Tags Python , Math Nice, it's shorter too. einsum (e. dot() for Numpy, and tf. Tensor operation speed often varies with size The numpy. For Lniux, Use snippsat's tutorial here For windows use This Then, numpy should install with: Welcome to /r/LearnPython!. matmul differs from dot in two important ways: Difference on performance between numpy and matlab. zeros(A. C++, calling the BLAS functionalities through a shared object. -2*10**-16 is basically zero with some added floating point imprecision. IBM Watson Personality Insights vs NumPy VS. , numpy), depending on your package manager Rust vs ? benchmarks . Matrix multiplication to get the weighted vectors. I liked the space efficiency and speed of latin-1 strings by default. 5 以上。. makeyourownneuralnetwork opened this Issue May 23, 2017 · 12 comments Comments. We first import numpy. dot(vector_a, vector_b, out = None) returns the dot product of vectors a and b. It can also be called using self @ other in Python >= 3. Path objects can now be instantiated from str subclass instances (such as numpy. seed taken from open source projects. edu is a platform for academics to share research papers. making For instance, matrix multiplication, transposition, addition, etc. numpy. Machine learning frameworks like TensorFlow, PaddlePaddle, Torch, Caffe, Keras, and many others can speed up your machine learning development significantly. Numpy array vs list (self. Python Numpy Numba CUDA vs Julia vs IDL 26 September, 2018. Machine Learning Gist Raw. numpy dot vs matmul speed dot运算 numpy官方文档上所写： 如果 a 和 b都是 1-D arrays,它的作用是计算内积。(不进行复共轭) 如果 a 和 b 是 2-D arrays, 作用是矩阵的乘积， a 和 b的维数要满足矩阵乘积维数要求，此时推荐使用 matmul 或 a @ b 。 It is a function from NumPy package, which computes and returns dot product of two arrays numpy. To print floating point double precision numbers without losing precision, use the (es23. matmul() both are giving same results. 11. 5, use x @ y . pyplot as plt import numpy as np import time from PIL import Image from IPython import display # 为了render the frames 渲染frames import seaborn from collections import deque # 双端队列 %matplotlib inline seaborn. The GPU 1 is done by Tensorflow, which might not be very efficient. Related: by virtue of numeric libraries Numpy, Numba and the like. z = np. dot to get the dot product. i. I know benchmarking is a complicated issue; don't take my naive test too serious. dot() method. We instead use the dot function to compute inner products of vectors, to multiply a vector by a matrix, and to multiply matrices. sin(1) 10000000 loops, best of 3: 92. jpg chess2. This would allow for Matrix Multiply In this test, matrix multiplication code in Numba CUDA example is used; @jit decorator, @guvectorize decorator and @cuda. matmul #6961 Distributions Always enable grad when calculating lazy_property #7708import json import matplotlib. zeros([10])) y = tf. NumPy/MKL vs Matlab performance. Wolfram Community forum discussion about Wolfram Language vs. linalg or numpy. shape[1]) # Take each row of A for i in range(0, A. Task ¶4. This function is preliminary and included in NumPy 1. NumPy User Guide. dot function. That's couple to get this conversation started. Theano vs. try different values of L * **Vectorization helps a LOT with the speed** * Causes of mis-prediction Course 2: Improving Deep Neural Academia. Welcome to /r/LearnPython!. Scikit-learn vs NumPy VS. random. Use the following to do the same operation on the CPU: python matmul. rand(8,13 numpy. The output of the node is the composition of the dot or (scalar) product of a weights vector and the input vector and an activation function. here is some code: Assume matrices can fit in RAM: test on matrix 10*1000 x 1000. The computation between two or more arrays are very efficient and easy to implement. Stack Overflow help chat. Python built-ins vs. matmul() function returns the matrix product of two arrays. NumPy provides a compact, typed container for homogenous arrays of data. You can port an existing imperative code from numpy/pytorch/matlab by mechanically substituting correct API calls. D. Reduce¶. Issue #15002: urllib. I used np. Functions such as dot and inner often link to lightening-quick BLAS routines which can outperform einsum and certainly shouldn’t be forgotten about. Consider for example, computing the dot product of two large matrices with a small result:. predict(X), Y) Important Caveat: Standardize Your Predictors. mean() instead " So obviously the rolling mean isnt going to work with df4 but How can I fix this? Thanks a bunch in Advance! To multiply two matrices, dot method is used. dot-product, multiplication. Matrix multiplication is not commutative. response object to use _TemporaryFileWrapper (and _TemporaryFileCloser So the dot product from before has been replaced by a single matrix multiplication. Using NumPy. dot(X,Y). NumPy manual contents¶. It > might be possible to speed it up and I'm open to suggestions > > To see what I mean, try this. matmul() for TensorFlow. May 20, 2018 at 4:21 pm Calculating the Propagator: NumPy vs Fortran Functions Using F2PY Magic¶. If you want higher speed, you may want to try mkl library which has trigonometric functions that take a vector. dot(A, B) or torch. In most applications, your features will be measured on many different scales; however you’ll notice in the loss function described above, each $\beta_k$ parameter is being penalized by the same amount ($\lambda$). Ecosystem of tools to help you use TensorFlow Libraries & extensions Libraries and extensions built on TensorFlowNumpy: Plan for dropping Python 2. Compile and Execute Python Code Online. "numpy_array and simple_bool" vs "simple_bool and numpy_array", where "numpy_array" has the non-shortcircuitingThus, your convergence process is going to take an “inertial speed”, that is going to make it resilient against the noise that it will encounter in the “mud” of the initial transient phase. dot (and/or np. name: title layout: true class: center, middle, title count: false --- ## Chapel’s Home in the New Landscape of Scientific Frameworks ### (and what it can learn from the neighbolatest hyperlearn. inv(M), be concatenated using numpy. dot and torch. This exercise was started as demonstration for incorporating f2pymagic in an IPython notebook to utilize the great increase in speed it can provide over Python. python matmul. I am observing that on my machine tf. dot). dot…This article explains the new features in Python 3. float64 taken from open source projects. set() 游戏的界面，状态，执行动作之后的状态的变动，也就是游戏 …Cloud and HPC Solutions for Science. NumPy provides an excellent library for easy (in terms of writing code) and fast (in terms of speed) computations. For general tips and tricks to improve the performance of your Python programs see Finding the row and column of the min or max value of an array or matrix. dot: alternative matrix product with different broadcasting rules. matmul differs from dot in two important ways: If you need optimal speed for large stacks of small matrices on numpy right now, I'd try np. Because TensorFlow is very version specific, you'll have to go to the CUDA ToolKit Archive to download the version thatHere are the examples of the python api numpy. Comparisons against pure Python, Matlab, gfortran, Intel Fortran, Intel Fortran with MKL, and Java. 0: Broadcasting rules apply, see the numpy. # Predicted Y vs. einsum. CPU GPU only provides a speed up of around 4-5 times. The setting. MATLAB vs NumPy VS. einsum("ink,ikm", x, y)), or possibly trying the anaconda builds of numpy that use MKL, to check if MKL handles the small matrices better than OpenBLAS does. There might be as much as a 3x speed up by doing so: Pure Python vs NumPy vs TensorFlow Performance Comparison because it requires a dot product of an entire column of ones with another implemented with tf. tensor. We now give performance numbers on out-of-core matrix-matrix multiplication. One objective of Numba is having a seamless integration with NumPy. 5, compared to 3. Please ensure that you have met the prerequisites below (e. The most important aspect of Numpy arrays is that they are optimized for speed. well-understood function taking up most of our time gives a very clear path to optimizing for speed and power usage, both with better software Fortran Best Practices Omitting the dot in the literal constant is also incorrect. 57 ms) on a simple vector operation (your mileage will vary!). Convolutional neural networks for artistic style transfer b = tf. Numpy dot product . mv(A, B) or torch. Matrix Multiplication in Python. In this Still doesn't recognize numpy. In this paper I presented the Recurrent Error-driven Adaptive Control Hierarchy (REACH) model. Matrix Multiplication: special strategies may be necessary to get a speed advantage. The guide goes into quite a bit of explicit detail about how Numpy arrays are constructed and stored in memory and always explains the underlying reasons why some operations are faster than others. matmul ¶ numpy. Numpy - Coding on Simple Neural Network. inner (a, b) Inner product of two arrays. I replaced matmul with einsum and now I could get the same performance Sep 20, 2016 I have accidentally discovered a confusing difference in numpy v1. 2. Dot : Elapsed time is 0. In Python, the simplest way to do with is with the NumPy library. > Travis, I think I got the gotcha! The problem is the in the dot function. each filter computes a sort of volumetric dot product with the input to produce a 2D output, and when we stack Dedicated overloadable boolean operators Showing 1-67 of 67 messages. str_). [1] This only scratches the surface. After matrix multiplication the prepended 1 is removed. However, each time I try to use any of numpy functions (like, matmul(), dot(), concatenate()), the IPython kernel dies and restarts. matmul with scipy. We can further speed up the training at the cost of accuracy if we retrain the last few fully connected layers only. 7 support (github. Speed up numpy indexing resulting from unravel_index? Is there a way to speed up this indexing at all? The rest of the code involves matrix multiplication (i To perform standard matrix multiplication you world use np. For 1-D arrays, it is the inner product of the vectors. repeat(). hyperlearn package. Is matrix multiplication just a special case of the dot product of two sets of vectors when the sets of Dot product versus matrix multiplication, is the later a svd error checking vs. I have only skimmed on the _dotblas code, but I assume it try to find the right blas function based on the arguments ranks. Please read the rules and guidelines below and search before posting. matlab Hi David, Thank you for the reply which is useful. f90 (one uses the Fortran's built-in matmul and dot_product, the other implements these two functions in the code -- with no difference in speed). cosine method vs. from numpy import matrix from numpy import transpose from numpy import matmul from nu I am trying to speed up the multiplication because the size n for me is large (~10k). jpg c6thrust. 5. matmul (input, weights) + biases if …Please email to Grégoire Mesnil (first-add-a-dot-last-add-at-gmail-add-a-dot-com) for any problem report or feedback. vdot(vector_a, vector_b) returns the dot product of vectors a and b. Then we loop through each word in the vocabulary, grabbing the word vector associated with that word by using the wv dictionary. shape[0]): Firstly, you can directly subtract numpy arrays; no need for numpy. g. Pre-trained models and datasets built by Google and the communityOverview; add_metrics; BaselineEstimator; binary_classification_head; boosted_trees_classifier_train_in_memory; boosted_trees_regressor_train_in_memoryEigen offers matrix/vector arithmetic operations either through overloads of common C++ arithmetic operators such as +, -, *, or through special methods such as dot(), cross(), etc. dot(a, b) Interfa Stack Overflow. dot: If both a and b are 1-D (one dimensional) arrays — Inner product of two vectors (without a complex conjugation) If both a and b are 2-D (two dimensional) arrays — Matrix multiplication Wolfram Community forum discussion about Wolfram Language vs. matmul() - Learn NumPy in simple and easy steps starting from basic to Indexing and Slicing, Advanced Indexing, Broadcasting, Iterating Over Array, If both a and b are 2-D arrays, it is matrix multiplication, but using matmul or a In particular, it must have the right type, must be C-contiguous, and its dtype must be the dtype that would be returned for dot(a,b). dot¶ numpy. Python) submitted 1 year ago by [deleted] Numpy arrays can be multidimensional and are used mostly to do fast numeric operations. rand(d0, d1, …, dn) : creates an array of specified shape and The cos_matrix_multiplication function is clearly the fastest of these, but I'm wondering if you have suggestions of further efficiency improvements for matrix vector cosine distance calculations. dot(M, v) for shape (4, \*) column vectors, respectively numpy. the NumPy and the TensorFlow. With NumPy arrays, operations on elements can be faster because elements are regularly spaced in memory and more operations are performed through specialized C functions instead of Python loops. Until now, you've always used numpy to build neural networks. OpenCV vs NumPy About Us Lab 2 NumPy and SciPy (A,B) or A. out some output from curve. 安装的是 tensorflow 的 gpu 版本. Speed comparison with Project Euler: C vs Python vs Erlang vs Haskell Numpy array and matrix performance matrix multiplication vs. Appreciate if anyone can explain the difference below: import numpy as np a1 = np. outer (a, b[, out]) Compute the outer product of two vectors. 22 May 2016 The @ operator calls the array's __matmul__ method, not dot . The contestants. Apr 18, 2017 I encountered a curious performance issue in numpy. by Renato The operations are optimized to run with blazing speed by relying on the projects BLAS and LAPACK for underlying this is actually not all that efficient, because it requires a dot product of an entire column of ones with another vector (err), and we know that Speed of Matlab vs. identity(2), np. Meta Stack Overflow Why is matrix multiplication faster with numpy than with ctypes in Python? the good way to check the speed of an alg is big-oh, not the language. NumPy and numba ¶ from __future__ numba version: 0. observed Y plt. Eg. To do a matrix multiplication or a matrix-vector multiplication we use the np. one for each neuron, calculated by doing a dot product of its weights and the input values. matmul links 2 tensors to create a matrix multiplication We can freeze those layers from updates. dot instead. Reduce is a really useful function for performing some computation on a list and returning the result. sin(1) 100000 loops, best of 3: 2. (0,0) (1,0) (0,1) Additional binary graphics files are: pi. 3 exposed a problematic use of a gcc attribute used to prefer code size over speed in module initialization Install CUDA ToolKit The first step in our process is to install the CUDA ToolKit, which is what gives us the ability to run against the the GPU CUDA cores. The GPU 2 is done by Using NumPy. Python, calling the BLAS functionalities through a shared object. Be aware that enabling IntelliSense (/FR flag) is known to trigger some internal compilation errors. Apr 18, 2017 I encountered a curious performance issue in numpy. I haven't looked at Rust for a while. The line chart is based on worldwide web search for the past 12 months. Following normal matrix multiplication rules, a (n x 1) vector is expected, but I simply cannot find any information about how this is done in Python's Numpy module. I'm using Spyder IDE on Mac OS X, both Keras, Tensorflow, and numpy are up-to-date. The content of matrix_multiply2. dot (X, Y) A new hybrid front-end seamlessly transitions between eager mode and graph mode to provide both flexibility and speed. matmul is apparently not using out = np. we will go through the code for a convolutional neural network. This can save you having to type a lot of transposes. 5+ matrix multiplication @ The last point makes it clear that dot and matmul methods behave differently when passed 3D (or higher dimensional) arrays. Numpy provides a high-performance multidimensional array and basic tools to compute with and manipulate these arrays. Oct 24, 2012 at 11:18 am: Hi, I was just looking at the einsum function. On python 2. Trying to Check Cov Matrix calculation from SVD using Numpy and then also using the built in numpy covariance function. matmul(A, B) or A @ B (nor in the original article between NumPy and Tensorflow). layer = tf. g. Allow passing an output tensor via out= keyword arugment in torch. matmul (x1, x2, /, out=None, *, casting='same_kind', order='K', dtype=None convention. I need obtain a "W" matrix of multiples matrix multiplications (all multiplications result in column vectors). The decomposition is performed using LAPACK routine _gesdd. The red dot represents the same physical point in the two images. dot with numpy. import numpy as np import time n = 10000 x = np. For N dimensions it is a sum product over the last axis of a and the second-to-last of b : dot(a, b)[i,j,k,m] = sum(a[i As we can see, Numpy has the shortest run-time. It is commonly used in machine learning and data science for a variety of calculations. einsum has C iteration speed, but the actual matrix multiplication cannot match BLAS, iteration plus dot is slow iterating, It is a function from NumPy package, which computes and returns dot product of two arrays numpy. dot(v, M. Numpy VS Tensorflow: speed on Matrix calculations using np. 5rc1 or later: argmin over the flattened tensor (like numpy) To remove the handling of the special cases of 0 and so get some small speed up and allow second derivative set no_zeros_in_inputs to True. $\endgroup$ – origimbo Sep 13 '17 at 22:54 my first suggestion is to update python to 3. ) If …Jul 1, 2016 in python numpy gpu speed parallel I recently had to compute many inner products with a given matrix $\Ab$ for many different vectors $\xb_i$, or $\xb_i^T \Ab \xb_i$ . It can be simply calculated with the help of numpy. 6. I did a lot of network protocol stuff and bytes() was a pain to use. sgemv(. This optimization has been extended to @, numpy. TensorFlow An essential part of any scientific software application is the ability to run quickly. If you have an Intel cpu, perhaps it is better optimized than libm. multipy "Multiply arguments element-wise". 2W^T$you need to perform the matrix Hello, I was playing with the matrix type in numpy and I felt the "need" to have the "dot" function returning a 1x1 matrix as a scalar. No module named numpy It means you need to install Numpy. matlab Sebastian Walter wrote: > On Thu, Jun 4, 2009 at 10:56 PM, Chris Colbert< [hidden email] > wrote: > >> I should update after reading the thread Sebastian linked: >> >> The current 1. Oliphant, Ph. Blue Speaker says. polynomial. Having to use the dot() function for matrix-multiply is messy -- dot(dot(A,B),C) vs. Note that . dot It appears that the same observation has been made by hpaulj and buried in a Apr 23, 2016 On numpy current master (da6e4c7), np. scatter(l2_mape_model. Tag: python,matlab,numpy,matrix. It was astonishingly difficult to find working code examples for this task Matrix Multiplication Benchmark. matmul() in NumPy - NumPy - NumPy Linear Algebra - numpy. Try to avoid for loops when you can. random((1,4)) Hello, I was playing with the matrix type in numpy and I felt the "need" to have the "dot" function returning a 1x1 matrix as a scalar. matmul to be at least as fast as when running the code using CPU (numpy). speed. Tensor operation speed often varies with size as some implemented arrays in NumPy are designed to be efficient when large. AIToolbox vs NumPy VS. For example, a matrix of shape 3x2 and a matrix of shape 2x3 can be multiplied, resulting in a matrix shape of 3 x 3. tensordot). And much, much faster! To measure the speed I created a large random data set, with 1 million rows of 10 parameters each. dot(A,v) Solving systems of equations with numpy. For N dimensions it is a sum product over the last axis of a and the second-to-last of b : dot(a, b)[i,j,k,m] = sum(a[i NumPy - NumPy Linear Algebra - numpy. you can call dot on the Java object. 1 llvm version: 0. dot (other) [source] ¶ Compute the matrix mutiplication between the DataFrame and other. multiply(a, b) or a * b is preferred. nn. dot(A, np. (dot products and vector Python Numpy Tutorial. This can also be seen graphically in Posts about Python written by travisdewolf. tf. Linear Algebra Shootout: NumPy vs. rand(8,13 numpy. assert_warns can now be used as a context manager This matches the behavior of assert_raises. Let's do it! numpy/scipy. 7. The above solution fits a polynomial of order 11. Note how slow was Python and how efficient was NumPy. matmul() - Learn NumPy in simple and easy steps starting from basic to Indexing and Slicing, Advanced Indexing, Broadcasting, Iterating Over Array, Jul 20, 2017 21 Matrix Multiplication and Numpy Dot. 7. dot After I made this change, the naïve for-loop and NumPy were about a factor of 2 apart, not enough to write a blog post about. Here’s the timings for the sample provided in the question – Prerequisite – numpy. I have googled a bit and found some platform specific solutions but nothing general. T) for shape (\*, 4) row vectors ("array of points"). Quoting from the documentation some more: Why is indicated airspeed …Numpy implementation using numpy. dot( a, b, out=None) Few specifications of numpy. py is: Data Science: Performance of Python vs Pandas vs Numpy calculated as processing speed measured against processing speed of pure Python. My non-regularized solution is coefficients = np. 16) from numpy cimport ndarray from numpy import empty cdef extern: void c_mesh_exp import numpy as np ''' The following code is to help you play with Numpy, which is a library that provides functions that are especially useful when you have to work with large arrays and matrices of numeric data, like doing matrix matrix multiplications. Vincenzo Lavorini Blocked I choose to use only matrix operations, in order to speed up the calculations. submitted 3 years ago by jdh30. I have ported this "in flavour" to ATI OpenCL and it is only 1/2 slower than the brook+ version. linalg moduleThe Numpy library is the defacto standard for manipulating matrices and vectors (and higher order tensors) from within Python. 1 版本 否则利用 python -m pip install -U pip 进行升级，或下载 pip 源文件; 确定你的显卡是否支持 cuda ，以及 cuda 版本。 利用 NVIDIA 控制面板查看，具体请百度。. [Numpy-discussion] einsum slow vs (tensor)dot; George Nurser. There might be as much as a 3x speed up by doing so: I used np. Essential for using TensorFlow pip3 install --upgrade matplotlib # Used for plotting data, which is very useful for machine learning pip3 install --upgrade pandas # Used for loading data sets the speed difference is negligible until we start building extremely large models with massive Posts about Allgemein written by Matthias Groncki. There are two ways to deal with matrices in numpy. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more. Alternatives to numpy. This will use the CPU with a matrix of size 1500 squared. If the first argument is 1-D, it is promoted to a matrix by prepending a 1 to its dimensions. Specifically, If both a and b are 1-D arrays, it is inner product of vectors (without complex conjugation). In an effort to further explore the benefits of Numba we decided to use a new code that implements floating point operations. (top of the book) shown in Figure 1. For very large arrays you should also notice a speed improvement over our Python-only version, thanks to NumPy's use of C code to implement many of its core functions and data structures. dot¶ DataFrame. using np. but it is equivalent to matmul and not dot Pure Python vs NumPy vs TensorFlow Performance Comparison. 7 (what Quantopian currently supports), the most efficient and idiomatic way to do matrix multiplication on numpy arrays is to use left. . matmulの速度比較をしてみた。一番遅いのがnumpyだというのは分かるが、skcudaとtensorflowはどっちが速いのか興味があった。Performance of Simple Use Case - PyTorch vs Plain Python with Numpy #1630. py gpu 1500. I've needed about five minutes for each of the non-library scripts and about 10 minutes for the NumPy/SciPy scripts. Loading Unsubscribe from Vidya Sagar? Cancel Unsubscribe. I want to pick a small k say 100 and really speed up the multiplication. 6 Aug 2017 One of the operations he tried was the multiplication of matrices, using np. com) 662 points by AndrewDucker 11 months Raw_input vs input could have been solved by slowly deprecating input. This image is only illustrative, a NumPy array may not necessarily be in C-order (more on that later): Recurring theme: NumPy lets us have the best of both worlds (high-level Python for development, optimized representation and speed via low-level C routines for Blog Explaining Tensorflow Code for a Convolutional Neural Network. In particular, it must have the right type, must be C-contiguous, and its dtype must be the dtype that would be returned for dot(a,b). dot(A, B) or np. If the second argument is 1-D, it is promoted to a matrix by appending a 1 to its dimensions. For example, if you wanted to compute the product of a list of integers. While it returns a normal product for 2-D arrays, if dimensions of either argument is >2, it is treated as a stack of matrices residing in the last two indexes and is broadcast accordingly. import numpy. time (); x. dot(right). 1 (but probably everywhere): >>> np. Stay on top of important topics and build connections by joining Wolfram Community groups relevant to your interests. I would remove the numpy commands from my very large program--but there are many of them. Submodules; hyperlearn. array(2)) In particular, it must have the right type, must be C-contiguous, and its dtype must be the dtype that would be returned for dot(a,b). f90 and spectral_norm6. And the difference in speed between these and the more naive algorithms are extremely striking. 1 version supports MSVC 2008. This would allow for Interest over time of SymPy and NumPy Note: It is possible that some search terms could be used in multiple areas and that could skew some graphs. The numba speed (the second entry for each value of n) up actually is very small at best, exactly as predicted by the numba project's documentation since we don't have "native" python code (we call numpy functions which can't be compiled in optimal ways). Hello list, Here's another idea resurrection from numpy github comments that I've been advised could be posted here for re-discussion. 4. The Re: performance matrix multiplication vs. subtract. dot(np. For N-dimensional arrays, it is a sum product over the last axis of a and the second-last axis of b. matmul (x1, x2, /, out=None, *, casting='same_kind', order='K', dtype= None, subok=True[, If not provided or None, a freshly-allocated array is returned. T) in numpy doesn’t get the (11 replies) Out of curiosity, I did a quick benchmark test of IDL, NumPy and Matlab on my desktop machine. Compare to NumPy arrays, happy contiguous chunks of memory, even across axes. when we calculate the dot product it’s a matrix multiplication of 5*5*3 sized chunk with 5*5*3 sized filter. Setting up. matlib import Numpy ndarray: np. matmul is now a ufunc which means that both the function and the __matmul__ operator can now be overridden It uses the same BLAS routines as numpy. 安装： 之前，TensorFlow还不支持Window系统,虽然可以安装 In order to calculate the dot product, it’s mandatory for the 3rd dimension of the filter to be same as the number of channels in the input. Now we will step you through a deep learning framework that will allow you to build neural networks more easily. But C++ is amazing when speed is important. Let’s do If you need optimal speed for large stacks of small matrices on numpy right now, I'd try np. About speed vs math Hello, I am giving some introduction tutorials to numpy and we notices a big difference in speed between nuumpy and math for trigonometric operations: In [3]: %timeit numpy. dot(vector_a, vector_b, out = None): returns the dot product of vectors a and b. dot It appears that the same observation has been made by hpaulj and buried in a Apr 23, 2016 On numpy current master (da6e4c7), np. Differences between numpy and matlab Dot Product / Matrix multiplication. The np. blas. dot function (05:14) Dot product 2: Speed comparison (01:39) numpy vs pytorch, pytorch basics, pytorch vs numpy. = tf. The tensordot function is also worth comparing for speed. Or in newer versions of numpy, simply use x. Numpy DOT vs Matmul: shankar: 0: 458: Oct-24-2018, 06:32 AM Last Post: shankar : Dictionary or Numpy Array? Performance of Pandas Series vs NumPy Arrays September 5, 2014 September 5, 2014 jiffyclub python pandas numpy performance snakeviz I recently spent a day working on the performance of a Python function and learned a bit about Pandas and NumPy array indexing. multi_dot (arrays) Compute the dot product of two or more arrays in a single function call, while automatically selecting the fastest evaluation order. This post will go through an example of how to use numpy for dot product. We are going to implement a few computations and check the results. Numpy, making use only of the functionality of dot. May 22, 2016 The @ operator calls the array's __matmul__ method, not dot . The speed up can range from 3 to 15 times. 3 version of numpy (don't know about previous versions) uses >> the optimized Atlas BLAS routines for numpy. For general tips and tricks to improve the performance of your Python programs see Finding the row and column of the min or max value of an array or matrix. By following the matrix multiplication rule (mXn * nXp = mXp), I expected an array of dimension (10,). matmul operator Jelte referenced in the opening message of the thread) e. Finding eigenvalues, eigenvectors. The first thing you’ll notice when running GPU-enabled code is a large increase in output, compared to a normal TensorFlow script. If first argument is complex the complex conjugate Deep Learning Prerequisites: The Numpy Stack in Python 4. Speed increases can be obtained relatively easily with faster CPUs and more memory. our computations will be done through Linear Algebra and matrix arithmetic eg. 就像 Tensorflow 当中的 tensor 一样. The matrix class in numpy is all-but-deprecated. Multiple Matrix Multiplication in numpy « James Hensman’s Weblog […] I am observing that on my machine tf. Python Matrix and Introduction to NumPy NumPy is a powerful Python library that can greatly increase the speed and efficiency of processing large data sets. 5 For loop vs. matmul in tensorflow is running significantly slower than dot product in numpy. 所以神经网络的话, 当然是用 Torch 的 tensor 形式数据最好咯. python numpy Numpy dot product . 1. To me, it's a really elegant and clear way of doing array operations, which is the core of what numpy is about. We will be glad to hear from you. com/tensorflow/tensor2tensor/tree/master CS455 Selected Lecture Notes This is one big WEB page, used for printing RunThread. By the way, it is useless to combine Psyco and NumPy. dot (a, b, out=None) ¶ Dot product of two arrays. For 2-D vectors, it is the equivalent to matrix multiplication. I found on the matlab homepage the following example What this layer does is exactly matrix multiplication. NumPy arrays provide an efficient storage method for homogeneous sets of data. 14 Jan 2015 tl;dr We benchmark dask on an out-of-core dot product. if you understand Numpy arrays you are off to a good start and Numpy arrays are just really efficient multidimensional arrays. rand(8,13 18 Apr 2017 I encountered a curious performance issue in numpy. In the next section, we will review some strategies to help you navigate your way through arrays in higher dimensions. In computer vision jargon we call these corresponding points. vdot (a, b) Return the dot product of two vectors. What is NumPy? Why is NumPy Fast? Who Else Uses NumPy?The fastest gfortran versions are spectral_norm2. pandas. A NumPy array is a chunk of memory containing fixed-sized items. matmul、さらに、skcuda. Use of a NVIDIA GPU significantly outperformed NumPy. md Lesson 8- Intro to tensorflow Removing for loops- to improve the run time. The following are 50 code examples for showing how to use numpy. but when the vectors are already in the GPU the And maybe there is some faster function for matrix multiplication in python, because I still use numpy. svd (a Changed in version 1. einsum("ink,ikm", x, y)), or possibly trying the anaconda builds of numpy that use MKL, to check if MKL handles the small matrices better than OpenBLAS does. dot() with different dimensional arrays The main motivation for using arrays in this manner is speed. jit decorator are tested. If either a or b is 0-D (scalar), it is equivalent to multiply and using numpy. Vidya Sagar. pinv(). I tried to reproduce the results locally. Slicing slower than matrix multiplication?. if using np. We seek the vector x that solves the equation. ndarray which returns the dot product of two matrices. DataFrame. i runs from 5 to 500 with an increment of 5 and the matricies m1 and m2 are set up Is it possible to do this via numpy broadcasting? Solution: We could use broadcasted multiplication and then sum along the first axis for the first alternative. Two matrices can be multiplied using the dot() method of numpy. dot() in Python numpy. (x,y) , accelaration and speed ? Reply. 0 for testing and Difference on performance between numpy and matlab. Given that most of the optimization seemed to be focused on a single matrix multiplication, let’s focus on speed in matrix multiplication. Difference between numpy dot() and Python 3. Therefore. dot(A, B) Theano: theano. dot in special case: for$V \in R^{n \times n}$and$U \in R^n\$, compare the speed up for numpy. You may be intending matrix multiplication, which is provided by numpy. April 20, 2015 By Pete Warden in Uncategorized 26 Comments. We slide the filter across the width and height of the input and compute the dot products between the entries of the filter and input at each position. AI Nano- Deep Learning. I understand that Matlab and numpy’s eig, svd, matrix multiplication numpy vs julia benchmarking for random matrix-vector multiplication numpy. They also don’t seem to play well with Python libraries such as numpy, scipy, scikit-learn, Cython and so on. The loop-in-Python method takes ~2. from numpy import matrix from numpy import transpose from numpy import matmul from nu Scalar * matrix multiplication is a mathematically and algorithmically distinct operation from matrix @ matrix multiplication, and is already covered by the elementwise * operator. I've needed about five minutes for each of the non-library scripts and about 10 minutes for the NumPy/SciPy scripts. This einsum with a k contraction, contains both the Out[9] and Out[32] values: When to use threads vs processes? Processes speed up Python operations that are CPU intensive because they *For certain operations like Dot Product, Numpy works around Python’s GIL and NumPy is a powerful Python library that can greatly increase the speed and efficiency of processing large data sets. polyno Matrix Multiplication Introduction From Python to Cython Handling NumPy Arrays Build Setup for Numpy Declaring the Array Type Matrix Multiplication Our Own MatMul Parallel Threads with Cython Wrapping C Libraries G-Node Workshop—Trento 2010 24 / 33 out = np. We also of posts constructing an out-of-core nd-array using NumPy, Blaze, and dask. The following are 50 code examples for showing how to use numpy. I have tried using numpy sparse matrix which does make the dot product lot faster but it is very slow to convert m1 and m2 into sparse vectors. dot for small block matrix multiplication. numpy matrix vector multiplication [duplicate] it manually to preserve the speed of the program. eye(). I am a beginner of Python


text:
## CryptoDB

### Paper: Towards a Formal Treatment of Logic Locking

Authors: Peter A. Beerel , University of Southern California Marios Georgiou , Galois, Inc. Ben Hamlin , Galois, Inc. Alex J. Malozemoff , Galois, Inc. Pierluigi Nuzzo , University of Southern California Search ePrint Search Google Logic locking aims to protect the intellectual property of a circuit from a fabricator by modifying the original logic of the circuit into a new locked'' circuit such that an entity without the key should not be able to learn anything about the original circuit. While logic locking provides a promising solution to outsourcing the fabrication of chips, unfortunately, several of the proposed logic locking systems have been broken. The lack of established secure techniques stems in part from the absence of a rigorous treatment toward a notion of security for logic locking, and the disconnection between practice and formalisms. We seek to address this gap by introducing formal definitions to capture the desired security of logic locking schemes. In doing so, we investigate prior definitional efforts in this space, and show that these notions either incorrectly model the desired security goals or fail to capture a natural compositional'' property that would be desirable in a logic locking system. Finally we move to constructions. First, we show that universal circuits satisfy our security notions. Second, we show that, in order to do better than universal circuits, cryptographic assumptions are necessary.
##### BibTeX
@article{tches-2022-31805,
title={Towards a Formal Treatment of Logic Locking},
journal={IACR Transactions on Cryptographic Hardware and Embedded Systems},
publisher={RUB},
author={Peter A. Beerel and Marios Georgiou and Ben Hamlin and Alex J. Malozemoff and Pierluigi Nuzzo},
year=2022
}

format: plain


text:
Zoe needs to buy beads to make jewelry. For each earring, Zoe uses 3 small blue beads, 1 small metallic bead, and 2 large green beads. What is the total number of beads Zoe will need to buy to make 8 pairs of earrings?

Question
Factors and multiples
Zoe needs to buy beads to make jewelry. For each earring, Zoe uses 3 small blue beads, 1 small metallic bead, and 2 large green beads. What is the total number of beads Zoe will need to buy to make 8 pairs of earrings?

2021-02-26
For each earring, the number of beads is 3+1+2=6
There are 2 earrings in a pair and since Zoe needs to make 8 pairs, there are a total of 2(8)=16 earrings. Hence, the total number of beads Zoe needs to buy is:
16(6)=96

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Case: Dr. Jung’s Diamonds Selection
With Christmas coming, Dr. Jung became interested in buying diamonds for his wife. After perusing the Web, he learned about the “4Cs” of diamonds: cut, color, clarity, and carat. He knew his wife wanted round-cut earrings mounted in white gold settings, so he immediately narrowed his focus to evaluating color, clarity, and carat for that style earring.
After a bit of searching, Dr. Jung located a number of earring sets that he would consider purchasing. But he knew the pricing of diamonds varied considerably. To assist in his decision making, Dr. Jung decided to use regression analysis to develop a model to predict the retail price of different sets of round-cut earrings based on their color, clarity, and carat scores. He assembled the data in the file Diamonds.xls for this purpose. Use this data to answer the following questions for Dr. Jung.
1) Prepare scatter plots showing the relationship between the earring prices (Y) and each of the potential independent variables. What sort of relationship does each plot suggest?
2) Let X1, X2, and X3 represent diamond color, clarity, and carats, respectively. If Dr. Jung wanted to build a linear regression model to estimate earring prices using these variables, which variables would you recommend that he use? Why?
3) Suppose Dr. Jung decides to use clarity (X2) and carats (X3) as independent variables in a regression model to predict earring prices. What is the estimated regression equation? What is the value of the R2 and adjusted-R2 statistics?
4) Use the regression equation identified in the previous question to create estimated prices for each of the earring sets in Dr. Jung’s sample. Which sets of earrings appear to be overpriced and which appear to be bargains? Based on this analysis, which set of earrings would you suggest that Dr. Jung purchase?
5) Dr. Jung now remembers that it sometimes helps to perform a square root transformation on the dependent variable in a regression problem. Modify your spreadsheet to include a new dependent variable that is the square root on the earring prices (use Excel’s SQRT( ) function). If Dr. Jung wanted to build a linear regression model to estimate the square root of earring prices using the same independent variables as before, which variables would you recommend that he use? Why?
1
6) Suppose Dr. Jung decides to use clarity (X2) and carats (X3) as independent variables in a regression model to predict the square root of the earring prices. What is the estimated regression equation? What is the value of the R2 and adjusted-R2 statistics?
7) Use the regression equation identified in the previous question to create estimated prices for each of the earring sets in Dr. Jung’s sample. (Remember, your model estimates the square root of the earring prices. So you must actually square the model’s estimates to convert them to price estimates.) Which sets of earring appears to be overpriced and which appear to be bargains? Based on this analysis, which set of earrings would you suggest that Dr. Jung purchase?
8) Dr. Jung now also remembers that it sometimes helps to include interaction terms in a regression model—where you create a new independent variable as the product of two of the original variables. Modify your spreadsheet to include three new independent variables, X4, X5, and X6, representing interaction terms where: X4 = X1 × X2, X5 = X1 × X3, and X6 = X2 × X3. There are now six potential independent variables. If Dr. Jung wanted to build a linear regression model to estimate the square root of earring prices using the same independent variables as before, which variables would you recommend that he use? Why?
9) Suppose Dr. Jung decides to use color (X1), carats (X3) and the interaction terms X4 (color * clarity) and X5 (color * carats) as independent variables in a regression model to predict the square root of the earring prices. What is the estimated regression equation? What is the value of the R2 and adjusted-R2 statistics?
10) Use the regression equation identified in the previous question to create estimated prices for each of the earring sets in Dr. Jung’s sample. (Remember, your model estimates the square root of the earring prices. So you must square the model’s estimates to convert them to actual price estimates.) Which sets of earrings appear to be overpriced and which appear to be bargains? Based on this analysis, which set of earrings would you suggest that Dr. Jung purchase?
Marianna is painting a ramp for the school play that is in the shape of a right triangular prism. The ramp has the dimensions shown. She will not pain the back or bottom surfaces of the ramp.
Each can of paint covers 1,000 square inches. What is the fewest number of full cans of paint Marianna will need? Circle the minimum number of cans.
You buy 50 shares of stock for $18.25 per share. One month later, the value of the stock is$18.98 per share.
a. The value of the stock continues to increase by the same dollar amount each month. How much will your investment be worth in 1 year?
b. The value of the stock continues to increase by the same percent each month. How much will your investment be worth in 1 year?
$$(1,\ 2),\ (7,\ 9.5),\ (4,\ 7),\ (2,\ 4.2),\ (6,\ 8.25),\ (3,\ 5.8),\ (5,\ 8),\ (8,\ 10),\ (0,\ 0)$$


text:
# With n a positive integer, evaluate the sum

With $n$ a positive integer, evaluate the sum

$$\binom{n}{0}-3\binom{n}{1}+3^2\binom{n}{2}+\cdots+(-1)^n3^n\displaystyle\binom{n}{n}=\sum_{k=0}^n(-3)^k\binom{n}{k}$$

Anyone know how to approach this problem?

-

From the definition of binomial coefficients, $$\sum_{k=0}^n {n\choose k}x^k=(1+x)^n.$$ For your problem, take $x=-3$ to conclude the sum is $(-2)^n$.

-
How did you come up with the value x=-3? –  Aaron Dec 14 '12 at 4:58
@Aaron, JohnD guessed it.The whole thing is pretty obvious once you recognize a pattern-a binomial expansion. –  Richard Nash Dec 14 '12 at 5:03
Look at the terms in your sum: the binomial coefficients are being multiplied by $(-3)^k$, $k=0,1,2,\dots,n$ each time. –  JohnD Dec 14 '12 at 5:04
Can you explain the alternating postive and negative sign? Or is it just for the second term –  Aaron Dec 14 '12 at 5:12
@Aaron: $(-3)^n=(-1)^n\cdot 3^n$, that's why you are seeing the alternating signs and increasing powers of 3. –  JohnD Dec 14 '12 at 16:21
Recall that $$(1-x)^n = \sum_{k=0}^n \dbinom{n}k (-x)^k$$


text:
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text:
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 Physics , 2005, Abstract: This is another approach to realize Maxwell's "demon" hypothesis. Two Ag-O-Cs thermal electron ejectors, A and B, are settled in a vacuum tube. A non-uniform magnetic field exerted on the tube provides a one-way channel for the thermal electrons. Ejector A, losing electrons, charges positively, while ejector B, getting electrons, charges negatively, resulting in an electric voltage. In flying from A to B, the speed of the electrons decreases, and part of their thermal kinetic energy converts into electric potential energy. Thus, the temperature of the whole electron tube drops down slightly, and that can be compensated by the heat attracted from the ambient air. The device can provide a small but macroscopic power to an external load, violating Kelvin's statement of the second law.
 Physics , 1996, DOI: 10.1016/S0920-5632(96)00728-1 Abstract: The interior of the color flux tube joining a quark pair can be probed by evaluating the correlator of pair of Polyakov loops in a vacuum modified by another Polyakov pair, in order to check the dual superconductivity conjecture which predicts a deconfined, hot core. We also point out that at the critical point of any 3D gauge theories with a continuous deconfining transition the Svetitsky-Yaffe conjecture provides us with an analytic expression of the Polyakov correlator as a function of the position of the probe inside the flux tube. Both these predictions are compared with numerical results in 3D Z2 gauge model finding complete agreement.
 Physics , 2001, Abstract: In these notes we discuss the origin of shot noise ('Schroteffekt') of vacuum tubes in detail. It will be shown that shot noise observed in vacuum tubes and first described by W. Schottky in 1918 is a purely classical phenomenon. This is in pronounced contrast to shot noise investigated in mesoscopic conductors which is due to quantum mechanical diffraction of electron waves.
 Advances in Materials Science and Engineering , 2012, DOI: 10.1155/2012/808210 Abstract: A solar cooling tube using thermal/vacuum emptying method was experimentally studied in this paper. The coefficient of performance (COP) of the solar cooling tube was mostly affected by the vacuum degree of the system. In past research, the thermal vacuum method, using an electric oven and iodine-tungsten lamp to heat up the adsorbent bed and H2O vapor to expel the air from the solar cooling tube, was used to manufacture solar cooling tubes. This paper presents a novel thermal vacuum combined with vacuum pump method allowing an increased vacuum state for producing solar cooling tubes. The following conclusions are reached: the adsorbent bed temperature of solar cooling tube could reaches up to 233°C, and this temperature is sufficient to meet desorption demand; the refrigerator power of a single solar cooling tube varies from 1？W to 12？W; the total supply refrigerating capacity is about 287？kJ; and the COP of this solar cooling tube is about 0.215. 1. Introduction With the improvement of people’s living standard, the demand for air conditioner is increasing. Use of CFCs for refrigeration compression has global warming potential (GWP) and ozone depletion potential (ODP), so their use should be minimized. The energy problem has become a major problem facing human development, and has led to efforts to reduce fossil fuel usage. Solar energy, one of the most abundant resources, has many advantages, most importantly that it is environmentally friendly. This has led to attention from the worldwide research community. Adsorption refrigeration uses nature working pairs as refrigerants and solar energy as a heat resource, so it consumes no fossil fuels during refrigeration process and is environment-friendly. Ferreira Leite et al. [1] presented the characterization and the pre-dimensioning of an adsorption chiller as part of a 20？kW air conditioning central unit for cooling a set of rooms that comprises an area of 110？m2. The adsorption chiller’s expected coefficient of performance (COP) was found to be around 0.6. Khattab [2] presented the description and operation of a simple structure, low cost solar-powered adsorption refrigeration module. Test results showed that a module using bed technique Type 4 and reflector arrangement Type C provided the best performance. Wang et al. [3, 4] used a compound adsorbent of CaCl2 and activated carbon as working pairs and to produce an ice-making test unit for fishing boats. At evaporating temperatures of ？35°C and ？25°C, the cooling powers are 0.89 and 1.18？kW respectively. Clausse [5] explored the possibility to perform
 Victor-Otto de Haan Physics , 2011, Abstract: A proposal for the realization of Santilli's comparative test of the gravity of electrons and positrons via a horizontal supercooled vacuum tube is described. Principle and requirements are described concerning the sources, vacuum chamber electromagnetic shielding and pressure and position sensitive detector. It is concluded that with current technology the experiment is perfectly feasible.
 Physics , 2007, DOI: 10.1142/S0218271809014273 Abstract: In this paper we analyse the effect produced by the temperature in the vacuum polarization associated with charged massless scalar field in the presence of magnetic flux tube in the cosmic string spacetime. Three different configurations of magnetic fields are taken into account: $(i)$ a homogeneous field inside the tube, $(ii)$ a field proportional to $1/r$ and $(iii)$ a cylindrical shell with $\delta$-function. In these three cases, the axis of the infinitely long tube of radius $R$ coincides with the cosmic string. Because the complexity of this analysis in the region inside the tube, we consider the thermal effect in the region outside. In order to develop this analysis, we construct the thermal Green function associated with this system for the three above mentioned situations considering points in the region outside the tube. We explicitly calculate in the high-temperature limit, the thermal average of the field square and the energy-momentum tensor.
 Physics , 2006, Abstract: We propose a new thermodynamic equality and several inequalities concerning the relationship between work and information for an isothermal process with Maxwell's demon. Our approach is based on the formulation a la Jarzynski of the thermodynamic engine and on the quantum information-theoretic characterization of the demon. The lower bound of each inequality, which is expressed in terms of the information gain by the demon and the accuracy of the demon's measurement, gives the minimum work that can be performed on a single heat bath in an isothermal process. These results are independent of the state of the demon, be it in thermodynamic equilibrium or not.
 Physics , 2013, DOI: 10.1017/S0022377813001256 Abstract: In this paper we analyze the motion of charged particles in a vacuum tube diode by solving linear differential equations. Our analysis is based on expressing the volume charge density as a function of the current density and coordinates only, while in the usual scheme the volume charge density is expressed as a function of the current density and electrostatic potential. Our approach gives the well known behavior of the classical current density proportional to the three-halves power of the bias potential and inversely proportional to the square of the gap distance between the electrodes, and does not require the solution of the nonlinear differential equation normally associated with the Child-Langmuir formulation.
 Physics , 2003, Abstract: This paper addresses two seemingly unrelated problems, (a) What is the entropy and energy accounting in the Maxwell Demon problem? and (b) How can the efficiency of markets be measured? Here we show, in a simple model for the Maxwell Demon, the entropy of the universe increases by an amount eta=0.839995520 in going from a random state to an ordered state and by an amount eta*=2.731382 in going from one sorted state to another sorted state. We calculate the efficiency of an engine driven by the Maxwell sorting process. The efficiency depends only on the temperatures of the particles and of the computer the Demon uses to sort the particles. We also show the approach is general and create a simple model of a stock market in which the Limit Trader plays the role of the Maxwell Demon. We use this model to define and measure market efficiency.
 Geoffrey Grimmett Mathematics , 2009, Abstract: A number of tricky problems in probability are discussed, having in common one or more infinite sequences of coin tosses, and a representation as a problem in dependent percolation. Three of these problems are of `Winkler' type, that is, they ask about what can be achieved by a clairvoyant demon.
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Paper Summary: Neural Machine Translation by Jointly Learning to Align and Translate

Last updated:

Please note This post is mainly intended for my personal use. It is not peer-reviewed work and should not be taken as such.

WHAT

Authors introduce attention, which is a way to enhance encoder-decoder architectures for sequence-to-sequence (seq2seq) learning. This is applied to Neural Machine Translation (NMT) as an example.

Attention mechanisms are a way to force models to select (attend to) some parts of the sequence input, rather than the whole input.

WHY

Because the traditional encoder-decoder architecture is limited by a bottleneck where information passes from input to output.

This happens because the input sequence is compressed to a single fixed-width vector and then it's converted into the output sequence.

HOW

In the new proposed architecture, the input sequence is first projected into multiple vectors and the attention mechanism learns to combine/choose from those to produce the output sequence.

In practice, what happens is that there is an individual fixed-width representation (a context) for each input element1.

These element-specific contexts are learned jointly with the seq2seq task and they are built off two components:

• 1) information about other elements surrounding element $$i$$ (called annotations on each element $$i$$)

• 2) information about how strong each element should impact the output token (i.e. weights)2

CLAIMS

• Proposed method outperforms vanilla RNN-based encoder-decoder

• Proposed method outperforms traditional phrase-based systems even though they are only evaluated in-vocabulary and use more data

• The performance of vanilla RNN-based encode-decoders drops dramatically with longer sequences, while that of the proposed method shows no such deterioration.

QUOTES

• Summary of attention mechanisms: "The most important distinguishing feature of this approach from the basic encoder–decoder is that it does not attempt to encode a whole input sentence into a single fixed-length vector. Instead, it encodes the input sentence into a sequence of vectors and chooses a subset of these vectors adaptively while decoding the translation."

NOTES

• In the proposed model, the architecture is still an encoder-decoder, but what's in the middle is not a single vector but multiple ones.

• By aligning, the authors mean matching words across different languages that can be in different order, such as the french word Zone that has been matched with Area, in order to translate European Economic Area into Zone Économique Européen.

• Here the word Zone is correctly matched (aligned) with Area, even though it appears at the end of one sentence and at the beginning of another.
• Visualizing the alignment weights for each word is very useful in the case of translating

• The attention mechanism (alignment model) is only used in the decoder model.

1: In traditional encoder-decoders, all input elements are squashed into a single fixed-width vector.

2: The weights are also learned via an embedded, small neural net referred to as a alignment model.


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Andrew Arnott
@AArnott
Thanks. Finding it in the .h files themselves is good justification as well. There are sometimes .h constants defined that aren't documented online.
We can always add an enum value later. And in the meantime, someone could cast the enum to an int and check for -1 to get unblocked if it really does work.
yep
Shimmy
@weitzhandler

Hi,
I'm unable to load the projects in VS 2019.
I'm getting this error in the Solution output:

The expression "[System.IO.Path]::GetDirectoryName('')" cannot be evaluated. The path is not of a legal form. C:\Users\Shimmy.nuget\packages\msbuild.sdk.extras\2.0.54\Sdk\Sdk.props
Thank you!

What I wanna contribute is User32.EnumDisplaySettings, and maybe another few.
Shimmy
@weitzhandler
Please mention me with @ if/when you reply, so I get an email notification. Thanks.
Andrew Arnott
@AArnott
@weitzhandler Have you run the init script at the root of the repo? The easiest way to work is run init -installlocality machine from an elevated prompt.
Shimmy
@weitzhandler
Shimmy
@weitzhandler
Please update this to include information on how to trigger the analyzers to generate the shipped/unshipped stuff
Shimmy
@weitzhandler
When pressing Ctrl+period over the relevant items in code issuing the RS0016 warnings, I see no offer to apply auto-fix
Andrew Arnott
@AArnott
I'll look into that Ctrl+period not working. Thanks for reporting, @weitzhandler .
@weitzhandler: update complete: dotnet/pinvoke@b8977c1
Shimmy
@weitzhandler
You're awesome, thank you!
Shimmy
@weitzhandler
Where do structures from wingdi.h go? It's mainly used in User32, should I add them to this project?
In fact, the DISPLAY_DEVICE structure also belongs there. It's used by the EnumDisplayDevice function that was merged. I can move these structures to Gdi32, but we'll have to add a project dependency.
Shimmy
@weitzhandler
I see that Gdi32 rather depends on User32, so I'll keep them all in User32
Shimmy
@weitzhandler
From what I gather, the only dependency of Gdi32 on User32 is the SafeDCHandle. Isn't it something common that should be abstracted out to a lower level shared project?
Because really looking at the Windows API it seems User32 depends on Gdi32, not the other way around.
Another option would be including these interfaces in the Windows.Core lib?
Andrew Arnott
@AArnott
@weitzhandler Ya, these issues are little cumbersome to deal with in .NET assemblies. SafeDCHandle is defined in User32 because ReleaseDC is defined there.
I would tend to define structures from wingdi.h in Gdi32, and only move them into the User32 assembly if User32 needs access to these types.
Perhaps where there are structures that might get moved 'up' toward Windows.Core we should define them as top-level types in the PInvoke namespace instead of as a nested type. That would make 'promoting' them later much easier.
Shimmy
@weitzhandler
So where do you want me to create those interfaces (i.e. DEVMODE), in Windows? Or Gdi32 (which in case I'm gonna have to move the SafeDCHandle over, because currently Gdi32 references User32, not the other way around 🤔
Shimmy
@weitzhandler
Ok, I'm gonna start by adding them to the Windows.Core lib.

How do you deal with fns that have moved around from one apiset dll to another. For e.g., kernel32!FlushProcessWriteBuffers:

When I looked at the exports, l1-1-0 had the export, but l1-1-2, l1-1-1 did not have the export (this was on recent Windows Insider Dev Branch build; so I guess the exports aren't preserved out of compat consideration etc.).

Andrew Arnott
@AArnott
@vatsan-madhavan I haven't noticed that they move around API sets like that, but I'd say let's keep them in the oldest location for Win8 apps so that they work everywhere, since I must assume Microsoft wouldn't release breaking changes.
:thumbsup:
Shimmy
@weitzhandler

Hi,
I don't know what I screwed up, but I'm unable to open any projects in VS2019.
I get the following error when running Init:

❯ .\init -installlocality machine
The netcore Credential Provider is already in C:\Users\Shimmy.nuget\plugins
Installing .NET Core SDK 3.1.100...
Installing .NET Core 2.1...
Installing .NET Core 2.1...
Restoring NuGet packages
D:\Users\Shimmy\Source\Repos\weitzhandler\pinvoke\src\BCrypt\BCrypt.csproj : warning MSB4242: The SDK resolver "NuGetSdkResolver" failed to run. Unable to find fallback package folder 'C:\Microsoft\Xamarin\NuGet\'.
Write-Error: Failure while restoring packages.

Any ideas?

This helped. Just had to create a dummy folder at that location
Shimmy
@weitzhandler
@AArnott
I started working on the DEVMODE structure (in Windows.Core).
Can you please have a look and tell me if I'm headed the right direction. Here's what I wrote.
I guess it'll all be chopped up into small files with DEVMODE_ prefixes (or you tell me how), but please compare my code to the actual DEVMODE.
I'll also format the enum comments etc. and make it look nice after I get some comments from you if this is how you'd want it to be.
Thank you!
Andrew Arnott
@AArnott
@weitzhandler Thanks for working on DEVMODE. Union structs are tricky. You'll need to use an Explicit layout in order for .NET to actually overlap fields over the same memory the way union structs do in native code.
Also, while I appreciate the extra structure you gave it (nested structs), it's beyond the original API and we generally aren't trying to improve on the original header file APIs. The precedent in this repo is that union structs simply contain every field as a flat list, exposing the overlapping members the same way the .h file does.
Shimmy
@weitzhandler
@AArnott The problem with these unions is that they contain identical fields, such as dmPosition.
Andrew Arnott
@AArnott
Those identical fields are placed in the same memory location though, I suspect. I checked and it's at least true for dmPosition. In C, there can't really be multiple identical fields because there would be no way to distinguish them. So while a single field may appear in multiple options in a union, it must be placed at exactly the same location so that it doesn't matter which one the user is thinking of when they access it.
Shimmy
@weitzhandler
Andrew Arnott
@AArnott
Done. See the PR for my comments.
Thought I'd leave a note that I'm still working on part-3 of #491 but haven't made much progress recently. I expect to get back to it slowly as my day-job lets up a bit :-).
Andrew Arnott
@AArnott
Sounds good, @vatsan-madhavan. Thanks for contributing.
I just submitted a significant enhancement to code-gen. We now have Span<T> and ReadOnlySpan<T> friendly overloads where any native pointer was that represented an array. The friendly overload even removes the "length" parameter as it takes it from the span.
Next up: #286 for string-friendly overloads. We almost don't need these with the span overloads already there, since .NET Core implicitly converts string to ReadOnlySpan<char>, but on .NET Framework it's still something of a mild pain.
Cristian Giagante
@cristiangiagante
hello everybody
Andrew Arnott
@AArnott
Hi @cristiangiagante
Zavarkó Gábor
@zgabi
Hi,
The code fix for RS0016 is not offered by my VS. Do you have any idea why? I selected net45 as it is in the documentation.. I have the latest VS 2019.
(I changed it to errors, but it was the same when it was only a warning)
Andrew Arnott
@AArnott
@zgabi sorry, I don't know what would cause that. That is pretty debilitating though, since adding the APIs to publicapi.txt is far too tedious without it. I guess go ahead and send the PR without it and I'll push a commit to your PR that adds it.
Zavarkó Gábor
@zgabi
Thanks. I created a PR
However I'm not sure how to name the files... There is a WindowsStyles enum, and each control types has extra values in this enum... I created new enums for each control type... another solution would be to add the values to the "base" WindowStyles enum, but in that case everything would be in the same file, which is not a good idea... there is no enum inheritance in C# :( Please check it out, and I'll rename the files or refactor it if you have better idea
@zgabi , There are two places in Visual Studio where net45 can be selected and only one of those works. When I come back to dotnet/pinvoke development after a break I sometimes get confused between the two and experience this problem - then belatedly remember that I should be selecting net45 at another spot ;-) Please see my screenshot.
Andrew Arnott
@AArnott


text:
# Hello, I am trying to sort the output of a query on the results of 2 fields.

Hello, I am trying to sort the output of a query on the results of 2 fields. The first field is Money owed, i've sorted this with > '0' in the criterion section of the field.
The second field is a calculation based on 2 fields in my table. The 2 fields are both yes/no fields. I am subtracting one field from the other. Depending on the state of the fields 3 outcomes are possible: 1, 0 & -1. If I leave the criterion empty in the second field all Monies owed are returned. If I put =’0’ in the criterion of the second field only Monies owed with a ‘0’ are returned. However if I put in the criterion field = ‘0’ OR = ‘1’ all of the Monies owed are returned that have a value of ‘0’ and ‘1’ irrespect of whether the value of the Monies owed is greater than 0. I want returned all Monies owed that have a value greater than ‘0’ in the first field and equal to 0 or 1 in the second and not show the -1 Monies owed. Can someone help please?

Below is an example of the 2 fields. I need the monies owed field greater than 0 and the indate field to show all 0 and 1's that correspond to Monies >0

Monies owed indate
£98.67             -1
£98.67             -1
£98.67              1
£98.67             0
£98.67             0
£98.67             1
£98.67             1
£98.67             1
£98.67             1

edit retag close merge delete

Edited for clarity.

( 2019-10-14 00:50:10 +0100 )edit

Hello,

Please, when dealing with Base, always mention database used as items such as SQL can vary with database. It is usually helpful to have OS and specific LO version (may have bug fixed is a different release).

( 2019-10-14 01:13:18 +0100 )edit

Sort by » oldest newest most voted

Hello,

Depending upon the database used, you would not (and possibly cannot) use bool fields to do math. Using a simple integer field:

select * from "MoniesOwed" where "amount" > 0 And "field1" - "field2" > -1

Edit:

After a brief thought, if you stick with bool fields you can use:

Select * from "MoniesOwed" where "amount" > 0 AND NOT("field1" is False AND "field2" is True)

This eliminates only possible negative result.

more

Have read yet again your question. Am not certain if my "field1" and "field2" are in correct sequence for what you need. If not, it is simply switching the two field names. Hopefully this is clear.

Also, the main question uses the term "sort" but I see no mention in the question about a specific sequence wanted. Can you clarify?

( 2019-10-14 02:17:08 +0100 )edit

Ratslinger, thanks for the answers above, I'm very new to this - has you can probably guess. So, with that in mind:
My operating system is Windows 10, the version of Base in LibreOffice is 6.3. I think that all this is correct. I'll try your suggestions and get back to you. Thanks for your help! Much appreciated.

( 2019-10-14 10:03:39 +0100 )edit

Thank you for the information but this is not all of it. As was stated database used was noted and you have not mentioned. This can make a difference on SQL presented. Note that Base is not a database but rather a front end to a database. The database can be any of many types - Firebird embedded, HSQLDB embedded, MySQL, PostgreSQL, etc.

And as a note, all this information should be posted with each question asked.

( 2019-10-14 10:20:01 +0100 )edit

Dear Ratslinger,
I've just realized that I know absolutely nothing about SQL ( I mean nothing, first exposure!) I tried your suggestions but they didn't work - operator error I suspect. However, it did something that made me think. I tweaked it and it seems to work.What I did is this:

SELECT "First name", "Surname", "Total_fee", "Commission", "Deposit_paid", "Balance_paid", "Total_fee" - "Commission" - "Deposit_paid" - "Balance_paid" AS "Monies owed", "Guests_have_left" - "Booking_cancel" FROM "Bookingstbl" WHERE ( "Total_fee" - "Commission" - "Deposit_paid" - "Balance_paid" > '0' AND ( "Guests_have_left" - "Booking_cancel" = '0' OR "Guests_have_left" - "Booking_cancel" = '1' ) )

Going forward, because I will probably need to get help again (no probably involved!), I'll always use the code to try and describe my problems.

What a brilliant site this is!

Thanks again Chem

( 2019-10-14 10:27:30 +0100 )edit


text:
# Continuity definition by image rather than pre-image [duplicate]

One definition of continuity is that the pre-image of an open set is open. Can I define Continuity as that the image of an open set is open AND the image of close set is close? If this is not correct, are there any counterexamples?

• What you are asking is if an open/closed map is necessarily continuous. Consider the floor function $\lfloor\cdot\rfloor:\mathbb{R} \to \mathbb{Z}$ where $\mathbb{Z}$ has the discrete topology. – anakhro Aug 13 '15 at 13:16

Hint: Consider the floor function $\lfloor\cdot\rfloor:\mathbb{R} \to \mathbb{Z}$ where $\mathbb{Z}$ is endowed with the discrete topology.