PI Science API
PI Science API is scientific library for JAVA. You need to add only one .jar to your application (how ? -> see here).
| Class | Description | Examples |
|---|---|---|
Math [pi.math.api] |
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| Matrices (PIMatrix, 1.0.0) |
Addition, subtraction, multiplication, operations between matrix and constant, transposion, opposition, inversion, determinant, rank. | -> examples |
| Cramer`s rule (PICramerRule, 1.0.0) |
Solution for linear equitions (Ax = B). | -> examples |
Regression [pi.regression.api] |
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| Linear regression (PILinearRegression, 1.0.1) |
Linear regression: |
-> examples |
| Polynomial regression (PIPolynomialRegression, 1.0.2) |
Polynomial regression (..by degree): |
-> examples |
| Exponential regression (PIExponentialRegression, 1.0.3) |
Exponential regression: |
-> examples |
| Exponential modified regression (PIExponentialModifiedRegression, 1.0.3) |
Exponential modified regression: |
-> examples |
| Power regression (PIPowerRegression, 1.0.3) |
Power regression: |
-> examples |
| Gompertz regression (PIGompertzRegression, 1.0.4) |
Gompertz regression, supported method PARTIAL_SUMS, PARTIAL_AVERAGES, SELECTED_POINTS: |
-> examples |
| Logistic regression (PILogisticRegression, 1.0.5) |
Logistic regression: |
-> examples |
Smoothing [pi.smoothing.api] |
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| Moving average (PIMovingAverageSmoothing, 1.0.6) |
Moving average. | -> examples |
| Median smoothing (PIMedianSmoothing, 1.0.6) |
Median smoothing. | -> examples |
| Simple exponencial smoothing (PISimpleExponentialSmoothing, 1.0.6) |
Simple exponential smoothing (Brown`s exponential smoothing); several methods for generating first value: |
-> examples |
| Double exponencial smoothing (PIDoubleExponentialSmoothing, 1.0.7) |
Double exponential smoothing, several methods for generating first value: |
-> examples |
Statistics [pi.statistics.api] |
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| Descriptive statistics (PIVariable, 1.0.0) |
Population/sample mean, geometric mean, min, max, sum, range, quartiles, mode, median, interquartile range, population/sample variance, population/sample standard deviation, ZScore, skewness, kurtosis. | -> examples |
| Statistics classes (PIStatisticsClasses, 1.0.0) |
Classes for histogram, generating interval (bins) (Sturge`s rule, Scott`s rule, Square-root rule, Rice`s rule, Doane`s rule, Freedman-Diaconis rule, auto rule), frequencies, relative frequencies, cululative relative frequencies. | -> examples |
Distribution [pi.distribution.api] |
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| Normal distribution (PINormalDistribution, 1.0.8) |
Get probability for Z, get Z for probability, calc probability density. | -> examples |
Scheduled areas for next development:
Integrals - numerical methods, regressions, correlations, interpolations, hypothesis testing, fractions support,
neural networks, graph algorithms, cluster analysis...
neural networks, graph algorithms, cluster analysis...
