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TensorLy is a Python library that aims at making tensor learning simple and accessible. It allows to easily perform tensor decomposition, tensor learning and tensor algebra. Its backend system allows to seamlessly perform computation with NumPy, PyTorch, JAX, MXNet, TensorFlow or CuPy, and run methods at scale on CPU or GPU. The only pre-requisite is to have Python 3 installed. The easiest way is via the Anaconda distribution.

machine-learning mxnet tensorflow numpy pytorch decomposition tensor-factorization tensor tensor-algebra tensorly tensor-learning tensor-decomposition cupy tensor-regressions tensor-methods jaxDecompose the matrix A into L and U, mutating L and U in-place.A is not modified.

ndarray crout matrix lu decomposition linalg linear algebra mathReturns true if the matrix has a solution, otherwise false.

ndarray linear algebra equations math linalg lu decomposition matrixDecomposes a 3D matrix, useful for animations. Code ported from W3 CSS Spec. PRs for more tests/robustness/optimizations welcome.You may also be interested in mat4-interpolate, mat4-recompose, and css-mat4.

4x4 decompose gl-mat4 mat4 decomposition interpolation lerp linear tween animate css matrixJust note that it's up to you to ndscratch.free() the solution you get back to prevent leaking memory.Given an L and U ndarrays from a decomposition and a vector B, solve the system LY = B for Y and UX = Y for X.

ndarray lu matrix decomposition linear equations mathLibrary for decomposing 2D polygons into convex regions. Then you can use the decomp global.

convex decomposition polygon 2dThis code is 100% JavaScript and works in both node.js and in a browser via browserify. Decomposes the polygon defined by the list of loops into a collection of rectangles.

rectangle rectilinear polygon decomposition mesh optimalThis command will run Clusterix on http://127.0.0.1:5000 where you will be able to use the interface to upload data files, and select the algorithms/options that you want.

clustering visualization visual-analytics tf-idf decomposition plotanomalize enables a tidy workflow for detecting anomalies in data. The main functions are time_decompose(), anomalize(), and time_recompose(). When combined, it’s quite simple to decompose time series, detect anomalies, and create bands separating the “normal” data from the anomalous data. Load the tidyverse and anomalize packages.

detect-anomalies decomposition iqr time-series anomaly-detection anomalyAn R interface for libeemd C library for ensemble empirical mode decomposition (EEMD) and its complete variant (CEEMDAN). These methods decompose possibly nonlinear and/or nonstationary time series data into a finite amount of components (called IMFs, insintric mode functions) separated by instantaneous frequencies. This decomposition provides a powerful method to look into the different processes behind a given time series, and provides a way to separate short time-scale events from a general trend. Here a CEEMDAN decomposition is performed for the UK gas consumption series (length n = 108). By default, ceemdan extracts [log_2(n)] components, so here we get five IMFs and the residual.

r time-series eemd emd decompositionPlease refer these papers: [cviu] [iros17] for details on the system design, these: [crv16][wacv17][crv17] for some performance results and this thesis for a comprehensive description. There is also a dedicated website where Doxygen documentation will soon be available along with detailed tutorials and examples. It also provides several datasets formatted to work with MTF. The library is implemented entirely in C++ though interfaces for Python and MATLAB are also provided to aid its use in research applications. A simple interface for ROS is likewise provided for seamless integration with robotics projects. In addition to the registration tracking modules, MTF comes bundled with several state of the art learning and detection based trackers whose C++ implementations are publicly available - DSST, KCF, CMT, TLD, RCT, MIL, Struck, FragTrack, GOTURN and DFT. It can thus be used as a general purpose tracking test bed too.

tracking thesis cpp paper ros decomposition image-registration homography mtfMatrix manipulation and computation library.

matrix ml machine-learning decomposition svd singular value evd eigenvalue lu qr cholesky data mining datamining machine learningPartitions a binary image into a non-overlapping collection of rectangles. Works both in node.js and in browserify. Decomposes the binary bitmap image into a collection of boxes.

bitmap box rectangle decomposition partition minimalAnimated Investment Management Research at Sov.ai, sponsoring open source AI, Machine learning, and Data Science initiatives. DataGene is developed to detect and compare dataset similarity between real and synthetic datasets as well as train, test, and validation datasets. You can read the report on SSRN for additional details. Datasets can largely be compared using quantitative and visual methods. Generated data can take on many formats, it can consist of multiple dimensions of various widths and heights. Original and generated datasets have to be transformed into an acceptable format before they can be compared, these transformation sometimes leads to a reduction in array dimensions. There are two reasons why we might want to reduce array dimensions, the first is to establish an acceptable format to perform distance calculations; the second is the preference for comparing like with like. You can use the MTSS-GAN to generate diverse multivariate time series data using stacked generative adversarial networks in combination with embedding and recurrent neural network models.

encoding finance data-structures decomposition model-checking similarity-measures dataset-generation distance-measures synthesizers similarity-score testing-framework synthetic-data predictive-maintenance synthetic-dataset-generation distance-calculations dataset-similarity transformation-recipes data-transformations
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