20 skills found
Logan-Shi / UAV Motion ControlMATLAB implementation of UAV (unmanned aerial vehicle) control simulation, with RRT (rapidly exploring random tree) for path planning, B-Spline for trajectory generation and LP (linear programming) for trajectory optimization.
learnedsystems / RadixSplineA Single-Pass Learned Index
mirsaeedi / Spline Curve FittingB-Spline, Bezier, and Linear/Non-Linear fitting (Approximation and Interpolation) algorithms are implemented in Javascript.
QuantEcon / BasisMatrices.jlRoutines for constructing BasisMatrices of different types (Chebyshev polynomials, B-Splines, piecewise linear, complete monomials, Smolyak...)
floswald / ApproXD.jlB-splines and linear approximators in multiple dimensions for Julia
rishabhdevyadav / MPC Trajectory TrackingLinearized unicyclic robot model to track cubic spline curve in python.
hmdolatabadi / LRS NF[AISTATS2020] The official repository of "Invertible Generative Modling using Linear Rational Splines (LRS)".
JuXinglong / TITL MARS OPTGlobal optimization using mixed integer quadratic programming on non-convex two-way interaction truncated linear multivariate adaptive regression splines
jacobp925 / CinematicsPlugin for creating linear & interpolated spline curve cinematics
Daniblit / Ensemble Predictive Model Forecasting AMGEN Stock Price At Year End 31sThe basis of this project involves analyzing Amgen future profitability based on its current business environment and financial performance. Technical Analysis, on the other hand, includes reading the charts and using statistical figures to identify the trends in the stock market. The dataset used for this analysis was downloaded from Yahoo finance for year 2009 to 2019. There are multiple variables in the dataset – date, open, high, low, volume. Adjusted close. The columns Open and Close represent the starting and final price at which the stock is traded on a day. High and Low represent the maximum, minimum price of the share for the day. The profit or loss calculation is usually determined by the closing price of a stock for the day, I used the adjusted closing price as the target variable. I downloaded data on the inflation rate, unemployment rate, Industrial Production Index, Consumer Price Index for All Urban Consumers: All Items and Real Gross Domestic Product as independent variables, Quarterly Financial Report: U.S. Corporations: Cash Dividends Charged to Retained Earnings All Manufacturing: All Nondurable Manufacturing: Chemicals: Pharmaceuticals and Medicines Industry, Producer Price Index by Industry: Pharmaceutical Preparation Manufacturing, 30-Year Treasury Constant Maturity Rate, and Producer Price Index by Industry: Pharmaceutical and Medicine Manufacturing Index. The independent variables are economic parameters which was obtained from Federal Reserve Economic Data (FRED) website. Methodology 1. Linear Regression: The linear regression model returns an equation that determines the relationship between the independent variables and the dependent variable. I used linear regression tool in Alteryx with ARIMA tool to forecast the stock prices for the year. The algorithm was trained with the historical data to see how the variables impact on the dependent variable. The test data was used to predict the adjusted closing price for the year and predicted a stock price of $193.38. 2. Support Vector Machines (SVM): Support Vector Networks (SVN), are a popular set of supervised learning algorithms originally developed for classification (categorical target) problems and can be used for regression (numerical target) problems. SVMs are memory efficient and can address many predictor variables. This model finds the best equation of one predictor, a plane (two predictors) or a hyperplane (three or more predictors) that maximally separates the groups of records, based on a measure of distance into different groups based on the target variable. A kernel function provides the measure of distance that causes to records to be placed in the same or different groups and involves taking a function of the predictor variables to define the distance metric. I used the SVM tool in Alteryx with ARIMA tool to forecast the stock prices for the year and predicted a stock price of $189.44. 3. Spline Model: The Spline Model tool was used because it provides the multivariate adaptive regression splines (or MARS) algorithm of Friedman. This statistical learning model self-determines which subset of fields best predict a target field of interest and can capture highly nonlinear relationships and interactions between fields. I used the Spline tool in Alteryx with ARIMA tool to forecast the stock prices for the year and predicted a stock price of $201.84. The results from the models was weighted by comparing the RMSE of each model. A lower RMSE indicates that the model’s predictions were closer to the actual values. However, a simpler model with the same RMSE as a more complex model is generally better, as simpler models are less likely to be overfit. Though the Spline model had a lower RMSE, the Linear Regression model had fewer variables. Thus, we combined the 3 models with the ARIMA forecast in a model ensemble, which allows us to use the results of multiple models. The forecasted stock price is $197.99 with 1.5% increase for 31st December 2019. Apart from economic parameters, stock price is affected by the news about the company and other factors like demonetization or merger/demerger of the companies. There are certain intangible factors which can often be impossible to predict beforehand hence the model predicts that the stock price of Amgen will continue to rise except there is a drastic downturn of the company.
danielrioslinares / Python LibraryA collection of python implementations using SWIG, Instant, F2PY... Optimization like Least Squares Levenberg-Marquardt. Boundary Value problem solvers. Integration Simpson/Trapezoidal. Interpolation like Cubic spline. Tridiagonal/pentadiagonal system of equations solver. Linear algebra like Matrix inversion (Gauss-Jordan) and much more
amacaluso / Quantum Splines For Non Linear ApproximationsThis repository contains the code to reproduce the results in the paper Quantum Splines for Non-Linear Approximation, under publication for the ACM International Conference on Computing Frontiers 2020.
mbojan / LsplineR package: Linear Splines with Convenient Parameterizations
ArgyrisNt / IGA With THB Splines Using Gsmo LibraryThis project is based on my Master thesis "Isogeometric Analysis with THB-splines in G+Smo library". I have solved many physical problems, like Stationary heat conduction, Convection diffusion, Linear elasticity, Scalar wave propagation. There is also an example of surface fitting with least-square method using THB-splines.
dpicone1 / PythonNumericalMethodsPython Notebooks to solve integrals, derivatives, zero of a function, linear and non-linear systems, optimization, non linear fitting, interpolation and splines
OliBomby / Bezier ApproximationLibrary for approximating arbitrary piecewise-linear paths with a single B-Spline curve.
curvedinf / K SplanifoldsLinear-scaling compute & memory spline manifold for regression and interpolation
dkaramit / SimpleSplinesA header only library for linear, and cubic spline interpolation in C++.
Michael-Belias / Nonlinear Treatment Effects In IPD MA An Introduction To Modelling Absolute Risk Differences Using Background: Modelling treatment effects is one of the opportunities offered by individual participant data (IPD) meta-analysis (MA). Analysing associations between outcomes and continuous patient characteristics may be challenging when non-linear associations are present. Here, splines seem to offer great flexibility but are rarely applied. Objective: To introduce modelling of nonlinear absolute treatment effects using restricted splines, B-splines, P-splines and Smoothing splines and different pooling methods in IPD-MA. Methods: We describe splines and illustrate their performance in an artificial single study. We describe two-stage methods based on pointwise and multivariate meta-analysis and a one-stage method based on generalised additive mixed effects models (GAMMs) to pool the results of multiple studies. We illustrate their performance on three IPD-MA scenarios of five studies each: one where only the associations differ across studies, one where only the ranges of the effect modifier differ and one where both differ. We evaluated splines and pooling approaches in an empirical example, modelling the risk of fever and/or ear pain in children with acute otitis media conditional to age. Results: Across the three IPD-MA scenarios results varied. Penalised splines were smoother than regression splines. In the first scenario, multivariate meta-analysis was most efficient, in the second and third scenario, pointwise meta-analysis was most flexible but showed discontinuous curves and GAMMs performed in between. In the empirical example, GAMMs especially combined with penalised splines was smoother and used all information available in contrast to pointwise and multivariate meta-analysis. Conclusion: Splines provide a helpful tool to capture nonlinear treatment effect differences in IPD-MA.
gaborbernat / BSplineLevelSetRegionalSegmentationThis is a C++ implementation of the Olivier Bernard, Denis Friboulet, Philippe Thévenaz, and Michael Unser, "Variational B-Spline Level-Set: A Linear Filtering Approach for Fast Deformable Model Evolution," IEEE Transcations On Image Processing, vol. 18, no. 6, pp. 1179-1191, June 2009.
