PI Science API is scientific library for .NET (Standard).
| Class | Description | Examples |
|---|---|---|
Math [namespace pi.science.math] |
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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 |
| Gamma function (PIGamma, 1.1.0) |
Gamma function: |
-> examples |
| Gamma incomplete function (PIGamma, 1.1.4) |
Gamma incomplete function:
 |
-> examples |
| Beta function (PIBeta, 1.1.0) |
Beta function: |
-> examples |
| Numerical integration: (PINumericalIntegrationTrapezoidal, 1.2.0) |
Trapezoidal method: |
-> examples |
| Numerical integration: (PINumericalIntegrationRectangle, 1.2.0) |
Rectangle method: |
-> examples |
| Numerical integration: (PINumericalIntegrationSimpson, 1.2.0) |
Simpson`s method: |
-> examples |
Discrete math [namespace pi.science.discretemath] |
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| Prime (PIPrime, 1.1.8) |
Working with primes. | -> examples |
| Prime factorization (PIPrimeFactorizationSimple, 1.1.8) |
Simple method for prime factorization. | -> examples |
| Prime factorization - Fermat (PIPrimeFactorizationFermat, 1.1.8) |
Fermat method for prime factorization. | -> examples |
Probability [namespace pi.science.probability] |
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| Probability utils (PIProbabilityUtils, 1.1.8) |
Factorial, Combination, Catalan number. | -> examples |
Regression [namespace pi.science.regression] |
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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 [namespace pi.science.smoothing] |
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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 [namespace pi.science.statistic] |
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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 [namespace pi.science.distribution] |
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| Normal distribution (PINormalDistribution, 1.0.8) |
Get probability for X, get X for probability, calc probability density:
|
-> examples |
| CHI-Square distribution (PICHISquareDistribution, 1.1.0) |
Get probability for X, get X for probability, calc probability density:
|
-> examples |
| Student distribution (PIStudentDistribution, 1.1.0) |
Get probability for X, get X for probability, calc probability density:
|
-> examples |
| F distribution (PIFDistribution, 1.1.0) |
Get probability for X, get X for probability, calc probability density:
|
-> examples |
| Log normal distribution (PILogNormalDistribution, 1.1.2) |
Get probability for X, get X for probability, calc probability density:
|
-> examples |
| Exponential distribution (PIExponentialDistribution, 1.1.4) |
Get probability for X (CDF), get X for probability (InverseCDF), calc probability density (PDF):
|
-> examples |
| Poisson distribution (PIPoissonDistribution, 1.1.4) |
Get probability for X (CDF), get X for probability (InverseCDF), calc probability density (PDF):
|
-> examples |
| Erlang distribution (PIErlangDistribution, 1.1.4) |
Get probability for X (CDF), get X for probability (InverseCDF), calc probability density (PDF):
|
-> examples |
| Weibull distribution (PIWeibullDistribution, 1.1.8) |
Get probability for X (CDF), get X for probability (InverseCDF), calc probability density (PDF):
|
-> examples |
| Rayleigh distribution (PIRayleighDistribution, 1.1.8) |
Get probability for X (CDF), get X for probability (InverseCDF), calc probability density (PDF):
|
-> examples |
| Pareto distribution (PIParentoDistribution, 1.2.0) |
Get probability for X (CDF), get X for probability (InverseCDF), calc probability density (PDF):
|
-> examples |
Hypothesis testing [namespace pi.science.hypothesistesting] |
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| Shapiro-Wilk (original) (PIShapiroWilk, 1.2.2) |
Shapiro-Wilk (original) test of normality:
|
-> examples |
| Shapiro-Wilk (expanded) (PIShapiroWilkExpanded, 1.2.4) |
Shapiro-Wilk (expanded) test of normality:
|
-> examples |
| Skewness test (PISkewnessTest, 1.2.4) |
Skewness test of normality:
|
-> examples |
| Kurtosis test (PIKurtosisTest, 1.2.6) |
Kurtosis test of normality:
|
-> examples |
| D`Agostino-Pearson (PIDAgostinoPearson, 1.2.6) |
D`Agostino-Pearson test of normality:
|
-> examples |
| Jarque-Bera (PIJarqueBera, 1.2.6) |
Jarque-Bera test of normality:
|
-> examples |
| Kolmogorov-Smirnov (PIKolmogorovSmirnov, 1.2.8) |
Jarque-Bera test of normality:
|
-> examples |
Scheduled areas for next development:
Integrals - numerical methods, correlations, interpolations, hypothesis testing, fractions support, neural networks, graph algorithms, cluster analysis...
