Search Results for "statistical"

Lets-Plot is a multiplatform plotting library based on the Grammar of Graphics. The library' design is heavily influenced by Leland Wilkinson work The Grammar of Graphics describing the deep features that underlie all statistical graphics.

DROP implements the model libraries and provides systems for fixed income valuation and adjustments, asset allocation and transaction cost analytics, and supporting libraries in numerical optimization and statistical learning. DROP is composed of four main libraries: [Asset Allocation] (https://lakshmidrip.github.io/DROP/AssetAllocationModule.html) [Fixed Income Analytics] (https://lakshmidrip.github.io/DROP/FixedIncomeModule.html) [Numerical Optimization] (https://lakshmidrip.github.io/DROP/NumericalOptimizerModule.html) [Statistical Learning] (https://lakshmidrip.github.io/DROP/StatisticalLearningModule.html)

Abu Quantitative Integrated AI Big Data System, K-Line Pattern System, Classic Indicator System, Trend Analysis System, Time Series Dimension System, Statistical Probability System, and Traditional Moving Average System conduct in-depth quantitative analysis of investment varieties, completely crossing the user's complex code quantification stage, more suitable for ordinary people to use, towards the era of vectorization 2.0. The above system combines hundreds of seed quantitative models, such as financial time series loss model, deep pattern quality assessment model, long and short pattern combination evaluation model, long pattern stop-loss strategy model, short pattern covering strategy model, big data K-line pattern Historical portfolio fitting model, trading position mentality model, dopamine quantification model, inertial residual resistance support model, long-short swap revenge probability model, strong and weak confrontation model, trend angle change rate model, etc.

This library allows you to generate a random number in the specified range from 0 to n, without using the built-in function Math.random (), and gets a random number from the processing time of polymorphic mathematical calculations, which depends on the current physical parameters of the CPU, RAM, and t .P. The resulting sequence corresponds to a discrete uniform distribution and is close to natural random sequences in terms of statistical randomness tests (LFSR, Approximate Entropy, DIEHARD Test).

This app is based on the android platform, realizes the character color test. Users through the app provides 30 multiple-choice, app automatic statistical character color to red, yellow, blue, green parameters, thus knowing their own character bias. At the same time the app provide red, yellow, blue, green four colour corresponding character interpretation.

...With the designer, you are able to create different types of decision trees, random forests, n-grams, pattern-matchers, conversational agents and all sorts of AI related algorithms. You can combine statistical approaches as well as pattern matchers or others. Do natural language processing, image or data analysis & interpretation,...