哎哟我去！发送成功了！这是为啥呢？首先证明这个类确是是可以发送邮件的了，那么下一步要细细的分析这两段代码区别在何处了。 我分别打印了两个类构造完成后的结果： public function actionMailer() { echo ‘<pre>’; var_dump(Yii::$app->mailer); }
MathPHP is the only library you need to integrate mathematical functions into your applications. It is a self-contained library in pure PHP with no external dependencies.
It is actively under development with development (0.y.z) releases.
use MathPHP\Arithmetic;
$³√x = Arithmetic::cubeRoot(-8); // -2// Sum of digits
$digit_sum = Arithmetic::digitSum(99): // 18
$digital_root = Arithmetic::digitalRoot(99); // 9// Equality of numbers within a tolerance
$x = 0.00000003458;
$y = 0.00000003455;
$ε = 0.0000000001;
$almostEqual = Arithmetic::almostEqual($x, $y, $ε); // true// Copy sign
$magnitude = 5;
$sign = -3;
$signed_magnitude = Arithmetic::copySign($magnitude, $sign); // -5
Finance
use MathPHP\Finance;
// Financial payment for a loan or annuity with compound interest
$rate = 0.035 / 12; // 3.5% interest paid at the end of every month
$periods = 30 * 12; // 30-year mortgage
$present_value = 265000; // Mortgage note of $265,000.00
$future_value = 0;
$beginning = false; // Adjust the payment to the beginning or end of the period
$pmt = Finance::pmt($rate, $periods, $present_value, $future_value, $beginning);
// Interest on a financial payment for a loan or annuity with compound interest.
$period = 1; // First payment period
$ipmt = Finance::ipmt($rate, $period, $periods, $present_value, $future_value, $beginning);
// Principle on a financial payment for a loan or annuity with compound interest
$ppmt = Finance::ppmt($rate, $period, $periods, $present_value, $future_value = 0, $beginning);
// Number of payment periods of an annuity.
$periods = Finance::periods($rate, $payment, $present_value, $future_value, $beginning);
// Annual Equivalent Rate (AER) of an annual percentage rate (APR)
$nominal = 0.035; // APR 3.5% interest
$periods = 12; // Compounded monthly
$aer = Finance::aer($nominal, $periods);
// Annual nominal rate of an annual effective rate (AER)
$nomial = Finance::nominal($aer, $periods);
// Future value for a loan or annuity with compound interest
$payment = 1189.97;
$fv = Finance::fv($rate, $periods, $payment, $present_value, $beginning)
// Present value for a loan or annuity with compound interest
$pv = Finance::pv($rate, $periods, $payment, $future_value, $beginning)
// Net present value of cash flows
$values = [-1000, 100, 200, 300, 400];
$npv = Finance::npv($rate, $values);
// Interest rate per period of an annuity
$beginning = false; // Adjust the payment to the beginning or end of the period
$rate = rate($periods, $payment, $present_value, $future_value, $beginning);
// Internal rate of return
$values = [-100, 50, 40, 30];
$irr = Finance:irr($values); // Rate of return of an initial investment of $100 with returns of $50, $40, and $30// Modified internal rate of return
$finance_rate = 0.05; // 5% financing
$reinvestment_rate = 0.10; // reinvested at 10%
$mirr = Finance:mirr($values, $finance_rate); // rate of return of an initial investment of $100 at 5% financing with returns of $50, $40, and $30 reinvested at 10%// Discounted payback of an investment
$values = [-1000, 100, 200, 300, 400, 500];
$rate = 0.1;
$payback = Finance::payback($values, $rate); // The payback period of an investment with a $1,000 investment and future returns of $100, $200, $300, $400, $500 and a discount rate of 0.10// Profitability index
$values = [-100, 50, 50, 50];
$profitability_index = profitabilityIndex($values, $rate); // The profitability index of an initial $100 investment with future returns of $50, $50, $50 with a 10% discount rate
use MathPHP\NumberTheory\Integer;
$n = 225;
// Prime factorization
$factors = Integer::primeFactorization($n);
// Perfect powers
$bool = Integer::isPerfectPower($n);
list($m, $k) = Integer::perfectPower($n);
// Coprime
$bool = Integer::coprime(4, 35);
// Even and odd
$bool = Integer::isEven($n);
$bool = Integer::isOdd($n);
Numerical Analysis – Interpolation
use MathPHP\NumericalAnalysis\Interpolation;
// Interpolation is a method of constructing new data points with the range// of a discrete set of known data points.// Each integration method can take input in two ways:// 1) As a set of points (inputs and outputs of a function)// 2) As a callback function, and the number of function evaluations to// perform on an interval between a start and end point.// Input as a set of points
$points = [[0, 1], [1, 4], [2, 9], [3, 16]];
// Input as a callback function
$f⟮x⟯ = function ($x) {
return $x**2 + 2 * $x + 1;
};
list($start, $end, $n) = [0, 3, 4];
// Lagrange Polynomial// Returns a function p(x) of x
$p = Interpolation\LagrangePolynomial::interpolate($points); // input as a set of points
$p = Interpolation\LagrangePolynomial::interpolate($f⟮x⟯, $start, $end, $n); // input as a callback function
$p(0) // 1
$p(3) // 16// Nevilles Method// More accurate than Lagrange Polynomial Interpolation given the same input// Returns the evaluation of the interpolating polynomial at the $target point
$target = 2;
$result = Interpolation\NevillesMethod::interpolate($target, $points); // input as a set of points
$result = Interpolation\NevillesMethod::interpolate($target, $f⟮x⟯, $start, $end, $n); // input as a callback function// Newton Polynomial (Forward)// Returns a function p(x) of x
$p = Interpolation\NewtonPolynomialForward::interpolate($points); // input as a set of points
$p = Interpolation\NewtonPolynomialForward::interpolate($f⟮x⟯, $start, $end, $n); // input as a callback function
$p(0) // 1
$p(3) // 16// Natural Cubic Spline// Returns a piecewise polynomial p(x)
$p = Interpolation\NaturalCubicSpline::interpolate($points); // input as a set of points
$p = Interpolation\NaturalCubicSpline::interpolate($f⟮x⟯, $start, $end, $n); // input as a callback function
$p(0) // 1
$p(3) // 16// Clamped Cubic Spline// Returns a piecewise polynomial p(x)// Input as a set of points
$points = [[0, 1, 0], [1, 4, -1], [2, 9, 4], [3, 16, 0]];
// Input as a callback function
$f⟮x⟯ = function ($x) {
return $x**2 + 2 * $x + 1;
};
$f’⟮x⟯ = function ($x) {
return 2*$x + 2;
};
list($start, $end, $n) = [0, 3, 4];
$p = Interpolation\ClampedCubicSpline::interpolate($points); // input as a set of points
$p = Interpolation\ClampedCubicSpline::interpolate($f⟮x⟯, $f’⟮x⟯, $start, $end, $n); // input as a callback function
$p(0) // 1
$p(3) // 16
Numerical Analysis – Numerical Differentiation
use MathPHP\NumericalAnalysis\NumericalDifferentiation;
// Numerical Differentiation approximates the derivative of a function.// Each Differentiation method can take input in two ways:// 1) As a set of points (inputs and outputs of a function)// 2) As a callback function, and the number of function evaluations to// perform on an interval between a start and end point.// Input as a callback function
$f⟮x⟯ = function ($x) {
return $x**2 + 2 * $x + 1;
};
// Three Point Formula// Returns an approximation for the derivative of our input at our target// Input as a set of points
$points = [[0, 1], [1, 4], [2, 9]];
$target = 0;
list($start, $end, $n) = [0, 2, 3];
$derivative = NumericalDifferentiation\ThreePointFormula::differentiate($target, $points); // input as a set of points
$derivative = NumericalDifferentiation\ThreePointFormula::differentiate($target, $f⟮x⟯, $start, $end, $n); // input as a callback function// Five Point Formula// Returns an approximation for the derivative of our input at our target// Input as a set of points
$points = [[0, 1], [1, 4], [2, 9], [3, 16], [4, 25]];
$target = 0;
list($start, $end, $n) = [0, 4, 5];
$derivative = NumericalDifferentiation\FivePointFormula::differentiate($target, $points); // input as a set of points
$derivative = NumericalDifferentiation\FivePointFormula::differentiate($target, $f⟮x⟯, $start, $end, $n); // input as a callback function// Second Derivative Midpoint Formula// Returns an approximation for the second derivative of our input at our target// Input as a set of points
$points = [[0, 1], [1, 4], [2, 9];
$target = 1;
list($start, $end, $n) = [0, 2, 3];
$derivative = NumericalDifferentiation\SecondDerivativeMidpointFormula::differentiate($target, $points); // input as a set of points
$derivative = NumericalDifferentiation\SecondDerivativeMidpointFormula::differentiate($target, $f⟮x⟯, $start, $end, $n); // input as a callback function
Numerical Analysis – Numerical Integration
use MathPHP\NumericalAnalysis\NumericalIntegration;
// Numerical integration approximates the definite integral of a function.// Each integration method can take input in two ways:// 1) As a set of points (inputs and outputs of a function)// 2) As a callback function, and the number of function evaluations to// perform on an interval between a start and end point.// Trapezoidal Rule (closed Newton-Cotes formula)
$points = [[0, 1], [1, 4], [2, 9], [3, 16]];
$∫f⟮x⟯dx = NumericalIntegration\TrapezoidalRule::approximate($points); // input as a set of points
$f⟮x⟯ = function ($x) {
return $x**2 + 2 * $x + 1;
};
list($start, $end, $n) = [0, 3, 4];
$∫f⟮x⟯dx = NumericalIntegration\TrapezoidalRule::approximate($f⟮x⟯, $start, $end, $n); // input as a callback function// Simpsons Rule (closed Newton-Cotes formula)
$points = [[0, 1], [1, 4], [2, 9], [3, 16], [4,3]];
$∫f⟮x⟯dx = NumericalIntegration\SimpsonsRule::approximate($points); // input as a set of points
$f⟮x⟯ = function ($x) {
return $x**2 + 2 * $x + 1;
};
list($start, $end, $n) = [0, 3, 5];
$∫f⟮x⟯dx = NumericalIntegration\SimpsonsRule::approximate($f⟮x⟯, $start, $end, $n); // input as a callback function// Simpsons 3/8 Rule (closed Newton-Cotes formula)
$points = [[0, 1], [1, 4], [2, 9], [3, 16]];
$∫f⟮x⟯dx = NumericalIntegration\SimpsonsThreeEighthsRule::approximate($points); // input as a set of points
$f⟮x⟯ = function ($x) {
return $x**2 + 2 * $x + 1;
};
list($start, $end, $n) = [0, 3, 5];
$∫f⟮x⟯dx = NumericalIntegration\SimpsonsThreeEighthsRule::approximate($f⟮x⟯, $start, $end, $n); // input as a callback function// Booles Rule (closed Newton-Cotes formula)
$points = [[0, 1], [1, 4], [2, 9], [3, 16], [4, 25]];
$∫f⟮x⟯dx = NumericalIntegration\BoolesRule::approximate($points); // input as a set of points
$f⟮x⟯ = function ($x) {
return $x**3 + 2 * $x + 1;
};
list($start, $end, $n) = [0, 4, 5];
$∫f⟮x⟯dx = NumericalIntegration\BoolesRuleRule::approximate($f⟮x⟯, $start, $end, $n); // input as a callback function// Rectangle Method (open Newton-Cotes formula)
$points = [[0, 1], [1, 4], [2, 9], [3, 16]];
$∫f⟮x⟯dx = NumericalIntegration\RectangleMethod::approximate($points); // input as a set of points
$f⟮x⟯ = function ($x) {
return $x**2 + 2 * $x + 1;
};
list($start, $end, $n) = [0, 3, 4];
$∫f⟮x⟯dx = NumericalIntegration\RectangleMethod::approximate($f⟮x⟯, $start, $end, $n); // input as a callback function// Midpoint Rule (open Newton-Cotes formula)
$points = [[0, 1], [1, 4], [2, 9], [3, 16]];
$∫f⟮x⟯dx = NumericalIntegration\MidpointRule::approximate($points); // input as a set of points
$f⟮x⟯ = function ($x) {
return $x**2 + 2 * $x + 1;
};
list($start, $end, $n) = [0, 3, 4];
$∫f⟮x⟯dx = NumericalIntegration\MidpointRule::approximate($f⟮x⟯, $start, $end, $n); // input as a callback function
Numerical Analysis – Root Finding
use MathPHP\NumericalAnalysis\RootFinding;
// Root-finding methods solve for a root of a polynomial.// f(x) = x⁴ + 8x³ -13x² -92x + 96
$f⟮x⟯ = function($x) {
return $x**4 + 8 * $x**3 - 13 * $x**2 - 92 * $x + 96;
};
// Newton's Method
$args = [-4.1]; // Parameters to pass to callback function (initial guess, other parameters)
$target = 0; // Value of f(x) we a trying to solve for
$tol = 0.00001; // Tolerance; how close to the actual solution we would like
$position = 0; // Which element in the $args array will be changed; also serves as initial guess. Defaults to 0.
$x = RootFinding\NewtonsMethod::solve($f⟮x⟯, $args, $target, $tol, $position); // Solve for x where f(x) = $target// Secant Method
$p₀ = -1; // First initial approximation
$p₁ = 2; // Second initial approximation
$tol = 0.00001; // Tolerance; how close to the actual solution we would like
$x = RootFinding\SecantMethod::solve($f⟮x⟯, $p₀, $p₁, $tol); // Solve for x where f(x) = 0// Bisection Method
$a = 2; // The start of the interval which contains a root
$b = 5; // The end of the interval which contains a root
$tol = 0.00001; // Tolerance; how close to the actual solution we would like
$x = RootFinding\BisectionMethod::solve($f⟮x⟯, $a, $b, $tol); // Solve for x where f(x) = 0// Fixed-Point Iteration// f(x) = x⁴ + 8x³ -13x² -92x + 96// Rewrite f(x) = 0 as (x⁴ + 8x³ -13x² + 96)/92 = x// Thus, g(x) = (x⁴ + 8x³ -13x² + 96)/92
$g⟮x⟯ = function($x) {
return ($x**4 + 8 * $x**3 - 13 * $x**2 + 96)/92;
};
$a = 0; // The start of the interval which contains a root
$b = 2; // The end of the interval which contains a root
$p = 0; // The initial guess for our root
$tol = 0.00001; // Tolerance; how close to the actual solution we would like
$x = RootFinding\FixedPointIteration::solve($g⟮x⟯, $a, $b, $p, $tol); // Solve for x where f(x) = 0
Probability – Combinatorics
use MathPHP\Probability\Combinatorics;
list($n, $x, $k) = [10, 3, 4];
// Factorials
$n！ = Combinatorics::factorial($n);
$n‼︎ = Combinatorics::doubleFactorial($n);
$x⁽ⁿ⁾ = Combinatorics::risingFactorial($x, $n);
$x₍ᵢ₎ = Combinatorics::fallingFactorial($x, $n);
$！n = Combinatorics::subfactorial($n);
// Permutations
$nPn = Combinatorics::permutations($n); // Permutations of n things, taken n at a time (same as factorial)
$nPk = Combinatorics::permutations($n, $k); // Permutations of n things, taking only k of them// Combinations
$nCk = Combinatorics::combinations($n, $k); // n choose k without repetition
$nC′k = Combinatorics::combinations($n, $k, Combinatorics::REPETITION); // n choose k with repetition (REPETITION const = true)// Central binomial coefficient
$cbc = Combinatorics::centralBinomialCoefficient($n);
// Catalan number
$Cn = Combinatorics::catalanNumber($n);
// Lah number
$L⟮n、k⟯ = Combinatorics::lahNumber($n, $k)
// Multinomial coefficient
$groups = [5, 2, 3];
$divisions = Combinatorics::multinomial($groups);
Probability – Continuous Distributions
use MathPHP\Probability\Distribution\Continuous;
// Beta distribution
$α = 1; // shape parameter
$β = 1; // shape parameter
$x = 2;
$beta = new Continuous\Beta($α, $β);
$pdf = $beta->pdf($x);
$cdf = $beta->cdf($x);
$μ = $beta->mean();
// Cauchy distribution
$x₀ = 2; // location parameter
$γ = 3; // scale parameter
$x = 1;
$cauchy = new Continuous\Cauchy(x₀, γ);
$pdf = $cauchy->pdf(x);
$cdf = $cauchy->cdf(x);
// χ²-distribution (Chi-Squared)
$k = 2; // degrees of freedom
$x = 1;
$χ² = new Continuous\ChiSquared($k);
$pdf = $χ²->pdf($x);
$cdf = $χ²->cdf($x);
// Dirac delta distribution
$x = 1;
$dirac = new Continuous\DiracDelta();
$pdf = $dirac->pdf($x);
$cdf = $dirac->cdf($x);
// Exponential distribution
$λ = 1; // rate parameter
$x = 2;
$exponential = new Continuous\Exponential($λ);
$pdf = $exponential->pdf($x);
$cdf = $exponential->cdf($x);
$μ = $exponential->mean();
// F-distribution
$d₁ = 3; // degree of freedom v1
$d₂ = 4; // degree of freedom v2
$x = 2;
$f = new Continuous\F($d₁, $d₂);
$pdf = $f->pdf($x);
$cdf = $f->cdf($x);
$μ = $f->mean();
// Gamma distribution
$k = 2; // shape parameter
$θ = 3; // scale parameter
$x = 4;
$gamma = new Continuous\Gamma($k, $θ);
$pdf = $gamma->pdf($x);
$cdf = $gamma->cdf($x);
$μ = $gamma->mean();
// Laplace distribution
$μ = 1; // location parameter
$b = 1.5; // scale parameter (diversity)
$x = 1;
$laplace = new Continuous\Laplace($μ, $b);
$pdf = $laplace->pdf($x);
$cdf = $laplace->cdf($x);
// Logistic distribution
$μ = 2; // location parameter
$s = 1.5; // scale parameter
$x = 3;
$logistic = new Continuous\Logistic($μ, $s);
$pdf = $logistic->pdf($x);
$cdf = $logistic->cdf($x);
// Log-logistic distribution (Fisk distribution)
$α = 1; // scale parameter
$β = 1; // shape parameter
$x = 2;
$logLogistic = new Continuous\LogLogistic($α, $β);
$pdf = $logLogistic->pdf($x);
$cdf = $logLogistic->cdf($x);
$μ = $logLogistic->mean();
// Log-normal distribution
$μ = 6; // scale parameter
$σ = 2; // location parameter
$x = 4.3;
$logNormal = new Continuous\LogNormal($μ, $σ);
$pdf = $logNormal->pdf($x);
$cdf = $logNormal->cdf($x);
$mean = $logNormal->mean();
// Noncentral T distribution
$ν = 50; // degrees of freedom
$μ = 10; // noncentrality parameter
$x = 8;
$noncenetralT = new Continuous\NoncentralT($ν, $μ);
$pdf = $noncenetralT->pdf($x);
$cdf = $noncenetralT->cdf($x);
$mean = $noncenetralT->mean();
// Normal distribution
$σ = 1;
$μ = 0;
$x = 2;
$normal = new Continuous\Normal($μ, $σ);
$pdf = $normal->pdf($x);
$cdf = $normal->cdf($x);
// Pareto distribution
$a = 1; // shape parameter
$b = 1; // scale parameter
$x = 2;
$pareto = new Continuous\Pareto($a, $b);
$pdf = $pareto->pdf($x);
$cdf = $pareto->cdf($x);
$μ = $pareto->mean();
// Standard normal distribution
$z = 2;
$standardNormal = new Continuous\StandardNormal();
$pdf = $standardNormal->pdf($z);
$cdf = $standardNormal->cdf($z);
// Student's t-distribution
$ν = 3; // degrees of freedom
$p = 0.4; // proportion of area
$x = 2;
$studentT = new Continuous\StudentT::pdf($ν);
$pdf = $studentT->pdf($x);
$cdf = $studentT->cdf($x);
$t = $studentT->inverse2Tails($p); // t such that the area greater than t and the area beneath -t is p// Uniform distribution
$a = 1; // lower boundary of the distribution
$b = 4; // upper boundary of the distribution
$x = 2;
$uniform = new Continuous\Uniform($a, $b);
$pdf = $uniform->pdf($x);
$cdf = $uniform->cdf($x);
$μ = $uniform->mean(b);
// Weibull distribution
$k = 1; // shape parameter
$λ = 2; // scale parameter
$x = 2;
$weibull = new Continuous\Weibull($k, $λ);
$pdf = $weibull->pdf($x);
$cdf = $weibull->cdf($x);
$μ = $weibull->mean();
// Other CDFs - All continuous distributions// Replace '$distribution' with desired distribution.
$inv_cdf = $distribution->inverse($target); // Inverse CDF of the distribution
$between = $distribution->between($x₁, $x₂); // Probability of being between two points, x₁ and x₂
$outside = $distribution->outside($x₁, $x); // Probability of being between below x₁ and above x₂
$above = $distribution->above($x); // Probability of being above x to ∞// Random Number Generator
$random = $distribution->rand(); // A random number with a given distribution
Probability – Discrete Distributions
use MathPHP\Probability\Distribution\Discrete;
// Bernoulli distribution (special case of binomial where n = 1)
$p = 0.3;
$k = 0;
$bernoulli = new Discrete\Bernoulli($p);
$pmf = $bernoulli->pmf($k);
$cdf = $bernoulli->cdf($k);
// Binomial distribution
$n = 2; // number of events
$p = 0.5; // probability of success
$r = 1; // number of successful events
$binomial = new Discrete\Binomial($n, $p);
$pmf = $binomial->pmf($r);
$cdf = $binomial->cdf($r);
// Categorical distribution
$k = 3; // number of categories
$probabilities = ['a' => 0.3, 'b' => 0.2, 'c' => 0.5]; // probabilities for categorices a, b, and c
$categorical = new Discrete\Categorical($k, $probabilities);
$pmf_a = $categorical->pmf('a');
$mode = $categorical->mode();
// Geometric distribution (failures before the first success)
$p = 0.5; // success probability
$k = 2; // number of trials
$geometric = new Discrete\Geometric($p);
$pmf = $geometric->pmf($k);
$cdf = $geometric->cdf($k);
// Hypergeometric distribution
$N = 50; // population size
$K = 5; // number of success states in the population
$n = 10; // number of draws
$k = 4; // number of observed successes
$hypergeo = new Discrete\Hypergeometric($N, $K, $n);
$pmf = $hypergeo->pmf($k);
$cdf = $hypergeo->cdf($k);
$μ = $hypergeo->mean();
// Multinomial distribution
$frequencies = [7, 2, 3];
$probabilities = [0.40, 0.35, 0.25];
$multinomial = new Discrete\Multinomial($probabilities);
$pmf = $multinomial->pmf($frequencies);
// Negative binomial distribution (Pascal)
$r = 1; // number of successful events
$P = 0.5; // probability of success on an individual trial
$x = 2; // number of trials required to produce r successes
$negativeBinomial = new Discrete\NegativeBinomial($r, $p);
$pmf = $negativeBinomial->pmf($x);
// Pascal distribution (Negative binomial)
$r = 1; // number of successful events
$P = 0.5; // probability of success on an individual trial
$x = 2; // number of trials required to produce r successes
$pascal = new Discrete\Pascal($r, $p);
$pmf = $pascal->pmf($x);
// Poisson distribution
$λ = 2; // average number of successful events per interval
$k = 3; // events in the interval
$poisson = new Discrete\Poisson($λ);
$pmf = $poisson->pmf($k);
$cdf = $poisson->cdf($k);
// Shifted geometric distribution (probability to get one success)
$p = 0.5; // success probability
$k = 2; // number of trials
$shiftedGeometric = new Discrete\ShiftedGeometric($p);
$pmf = $shiftedGeometric->pmf($k);
$cdf = $shiftedGeometric->cdf($k);
// Uniform distribution
$a = 1; // lower boundary of the distribution
$b = 4; // upper boundary of the distribution
$k = 2; // percentile
$uniform = new Discrete\Uniform($a, $b);
$pmf = $uniform->pmf();
$cdf = $uniform->cdf($k);
$μ = $uniform->mean();
Probability – Multivariate Distributions
use MathPHP\Probability\Distribution\Multivariate;
// Dirichlet distribution
$αs = [1, 2, 3];
$xs = [0.07255081, 0.27811903, 0.64933016];
$dirichlet = new Multivariate\Dirichlet($αs);
$pdf = $dirichlet->pdf($xs);
// Normal distribution
$μ = [1, 1.1];
$∑ = MatrixFactory::create([
[1, 0],
[0, 1],
]);
$X = [0.7, 1.4];
$normal = new Multivariate\Normal($μ, $∑);
$pdf = $normal->pdf($X);
Probability – Distribution Tables
use MathPHP\Probability\Distribution\Table;
// Provided solely for completeness' sake.
// It is statistics tradition to provide these tables.
// MathPHP has dynamic distribution CDF functions you can use instead.
// Standard Normal Table (Z Table)
$table = Table\StandardNormal::Z_SCORES;
$probability = $table[1.5][0]; // Value for Z of 1.50
// t Distribution Tables
$table = Table\TDistribution::ONE_SIDED_CONFIDENCE_LEVEL;
$table = Table\TDistribution::TWO_SIDED_CONFIDENCE_LEVEL;
$ν = 5; // degrees of freedom
$cl = 99; // confidence level
$t = $table[$ν][$cl];
// t Distribution Tables
$table = Table\TDistribution::ONE_SIDED_ALPHA;
$table = Table\TDistribution::TWO_SIDED_ALPHA;
$ν = 5; // degrees of freedom
$α = 0.001; // alpha value
$t = $table[$ν][$α];
// χ² Distribution Table
$table = Table\ChiSquared::CHI_SQUARED_SCORES;
$df = 2; // degrees of freedom
$p = 0.05; // P value
$χ² = $table[$df][$p];
Sequences – Basic
use MathPHP\Sequence\Basic;
$n = 5; // Number of elements in the sequence// Arithmetic progression
$d = 2; // Difference between the elements of the sequence
$a₁ = 1; // Starting number for the sequence
$progression = Basic::arithmeticProgression($n, $d, $a₁);
// [1, 3, 5, 7, 9] - Indexed from 1// Geometric progression (arⁿ⁻¹)
$a = 2; // Scalar value
$r = 3; // Common ratio
$progression = Basic::geometricProgression($n, $a, $r);
// [2(3)⁰, 2(3)¹, 2(3)², 2(3)³] = [2, 6, 18, 54] - Indexed from 1// Square numbers (n²)
$squares = Basic::squareNumber($n);
// [0², 1², 2², 3², 4²] = [0, 1, 4, 9, 16] - Indexed from 0// Cubic numbers (n³)
$cubes = Basic::cubicNumber($n);
// [0³, 1³, 2³, 3³, 4³] = [0, 1, 8, 27, 64] - Indexed from 0// Powers of 2 (2ⁿ)
$po2 = Basic::powersOfTwo($n);
// [2⁰, 2¹, 2², 2³, 2⁴] = [1, 2, 4, 8, 16] - Indexed from 0// Powers of 10 (10ⁿ)
$po10 = Basic::powersOfTen($n);
// [10⁰, 10¹, 10², 10³, 10⁴] = [1, 10, 100, 1000, 10000] - Indexed from 0// Factorial (n!)
$fact = Basic::factorial($n);
// [0!, 1!, 2!, 3!, 4!] = [1, 1, 2, 6, 24] - Indexed from 0// Digit sum
$digit_sum = Basic::digitSum($n);
// [0, 1, 2, 3, 4] - Indexed from 0// Digital root
$digit_root = Basic::digitalRoot($n);
// [0, 1, 2, 3, 4] - Indexed from 0
Sequences – Advanced
use MathPHP\Sequence\Advanced;
$n = 6; // Number of elements in the sequence// Fibonacci (Fᵢ = Fᵢ₋₁ + Fᵢ₋₂)
$fib = Advanced::fibonacci($n);
// [0, 1, 1, 2, 3, 5] - Indexed from 0// Lucas numbers
$lucas = Advanced::lucasNumber($n);
// [2, 1, 3, 4, 7, 11] - Indexed from 0// Pell numbers
$pell = Advanced::pellNumber($n);
// [0, 1, 2, 5, 12, 29] - Indexed from 0// Triangular numbers (figurate number)
$triangles = Advanced::triangularNumber($n);
// [1, 3, 6, 10, 15, 21] - Indexed from 1// Pentagonal numbers (figurate number)
$pentagons = Advanced::pentagonalNumber($n);
// [1, 5, 12, 22, 35, 51] - Indexed from 1// Hexagonal numbers (figurate number)
$hexagons = Advanced::hexagonalNumber($n);
// [1, 6, 15, 28, 45, 66] - Indexed from 1// Heptagonal numbers (figurate number)
$hexagons = Advanced::heptagonalNumber($n);
// [1, 4, 7, 13, 18, 27] - Indexed from 1// Look-and-say sequence (describe the previous term!)
$look_and_say = Advanced::lookAndSay($n);
// ['1', '11', '21', '1211', '111221', '312211'] - Indexed from 1// Lazy caterer's sequence (central polygonal numbers)
$lazy_caterer = Advanced::lazyCaterers($n);
// [1, 2, 4, 7, 11, 16] - Indexed from 0// Magic squares series (magic constants; magic sums)
$magic_squares = Advanced::magicSquares($n);
// [0, 1, 5, 15, 34, 65] - Indexed from 0// Perfect powers sequence
$perfect_powers = Advanced::perfectPowers($n);
// [4, 8, 9, 16, 25, 27] - Indexed from 0// Not perfect powers sequence
$not_perfect_powers = Advanced::notPerfectPowers($n);
// [2, 3, 5, 6, 7, 10] - Indexed from 0// Prime numbers up to n (n is not the number of elements in the sequence)
$primes = Advanced::primesUpTo(30);
// [2, 3, 5, 7, 11, 13, 17, 19, 23, 29] - Indexed from 0
Set Theory
use MathPHP\SetTheory\Set;
use MathPHP\SetTheory\ImmutableSet;
// Sets and immutable sets
$A = new Set([1, 2, 3]); // Can add and remove members
$B = new ImmutableSet([3, 4, 5]); // Cannot modify set once created// Basic set data
$set = $A->asArray();
$cardinality = $A->length();
$bool = $A->isEmpty();
// Set membership
$true = $A->isMember(2);
$true = $A->isNotMember(8);
// Add and remove members
$A->add(4);
$A->add(new Set(['a', 'b']));
$A->addMulti([5, 6, 7]);
$A->remove(7);
$A->removeMulti([5, 6]);
$A->clear();
// Set properties against other sets - return boolean
$bool = $A->isDisjoint($B);
$bool = $A->isSubset($B); // A ⊆ B
$bool = $A->isProperSubset($B); // A ⊆ B & A ≠ B
$bool = $A->isSuperset($B); // A ⊇ B
$bool = $A->isProperSuperset($B); // A ⊇ B & A ≠ B// Set operations with other sets - return a new Set
$A∪B = $A->union($B);
$A∩B = $A->intersect($B);
$A＼B = $A->difference($B); // relative complement
$AΔB = $A->symmetricDifference($B);
$A×B = $A->cartesianProduct($B);
// Other set operations
$P⟮A⟯ = $A->powerSet();
$C = $A->copy();
// Print a set
print($A); // Set{1, 2, 3, 4, Set{a, b}}// PHP Interfaces
$n = count($A); // Countable
foreach ($A as $member) { ... } // Iterator// Fluent interface
$A->add(5)->add(6)->remove(4)->addMulti([7, 8, 9]);
Statistics – ANOVA
use MathPHP\Statistics\ANOVA;
// One-way ANOVA
$sample1 = [1, 2, 3];
$sample2 = [3, 4, 5];
$sample3 = [5, 6, 7];
⋮ ⋮
$anova = ANOVA::oneWay($sample1, $sample2, $sample3);
print_r($anova);
/* Array ( [ANOVA] => Array ( // ANOVA hypothesis test summary data [treatment] => Array ( [SS] => 24 // Sum of squares (between) [df] => 2 // Degrees of freedom [MS] => 12 // Mean squares [F] => 12 // Test statistic [P] => 0.008 // P value ) [error] => Array ( [SS] => 6 // Sum of squares (within) [df] => 6 // Degrees of freedom [MS] => 1 // Mean squares ) [total] => Array ( [SS] => 30 // Sum of squares (total) [df] => 8 // Degrees of freedom ) ) [total_summary] => Array ( // Total summary data [n] => 9 [sum] => 36 [mean] => 4 [SS] => 174 [variance] => 3.75 [sd] => 1.9364916731037 [sem] => 0.6454972243679 ) [data_summary] => Array ( // Data summary (each input sample) [0] => Array ([n] => 3 [sum] => 6 [mean] => 2 [SS] => 14 [variance] => 1 [sd] => 1 [sem] => 0.57735026918963) [1] => Array ([n] => 3 [sum] => 12 [mean] => 4 [SS] => 50 [variance] => 1 [sd] => 1 [sem] => 0.57735026918963) [2] => Array ([n] => 3 [sum] => 18 [mean] => 6 [SS] => 110 [variance] => 1 [sd] => 1 [sem] => 0.57735026918963) )) */// Two-way ANOVA/* | Factor B₁ | Factor B₂ | Factor B₃ | ⋯Factor A₁ | 4, 6, 8 | 6, 6, 9 | 8, 9, 13 | ⋯Factor A₂ | 4, 8, 9 | 7, 10, 13 | 12, 14, 16| ⋯ ⋮ ⋮ ⋮ ⋮ */
$factorA₁ = [
[4, 6, 8], // Factor B₁
[6, 6, 9], // Factor B₂
[8, 9, 13], // Factor B₃
];
$factorA₂ = [
[4, 8, 9], // Factor B₁
[7, 10, 13], // Factor B₂
[12, 14, 16], // Factor B₃
];
⋮
$anova = ANOVA::twoWay($factorA₁, $factorA₂);
print_r($anova);
/* Array ( [ANOVA] => Array ( // ANOVA hypothesis test summary data [factorA] => Array ( [SS] => 32 // Sum of squares [df] => 1 // Degrees of freedom [MS] => 32 // Mean squares [F] => 5.6470588235294 // Test statistic [P] => 0.034994350619895 // P value ) [factorB] => Array ( [SS] => 93 // Sum of squares [df] => 2 // Degrees of freedom [MS] => 46.5 // Mean squares [F] => 8.2058823529412 // Test statistic [P] => 0.0056767297582031 // P value ) [interaction] => Array ( [SS] => 7 // Sum of squares [df] => 2 // Degrees of freedom [MS] => 3.5 // Mean squares [F] => 0.61764705882353 // Test statistic [P] => 0.5555023440712 // P value ) [error] => Array ( [SS] => 68 // Sum of squares (within) [df] => 12 // Degrees of freedom [MS] => 5.6666666666667 // Mean squares ) [total] => Array ( [SS] => 200 // Sum of squares (total) [df] => 17 // Degrees of freedom ) ) [total_summary] => Array ( // Total summary data [n] => 18 [sum] => 162 [mean] => 9 [SS] => 1658 [variance] => 11.764705882353 [sd] => 3.4299717028502 [sem] => 0.80845208345444 ) [summary_factorA] => Array ( ... ) // Summary data of factor A [summary_factorB] => Array ( ... ) // Summary data of factor B [summary_interaction] => Array ( ... ) // Summary data of interactions of factors A and B) */
Statistics – Averages
use MathPHP\Statistics\Average;
$numbers = [13, 18, 13, 14, 13, 16, 14, 21, 13];
// Mean, median, mode
$mean = Average::mean($numbers);
$median = Average::median($numbers);
$mode = Average::mode($numbers); // Returns an array — may be multimodal// Weighted mean
$weights = [12, 1, 23, 6, 12, 26, 21, 12, 1];
$weighted_mean = Average::weightedMean($numbers, $weights)
// Other means of a list of numbers
$geometric_mean = Average::geometricMean($numbers);
$harmonic_mean = Average::harmonicMean($numbers);
$contraharmonic_mean = Average::contraharmonicMean($numbers);
$quadratic_mean = Average::quadraticMean($numbers); // same as rootMeanSquare
$root_mean_square = Average::rootMeanSquare($numbers); // same as quadraticMean
$trimean = Average::trimean($numbers);
$interquartile_mean = Average::interquartileMean($numbers); // same as iqm
$interquartile_mean = Average::iqm($numbers); // same as interquartileMean
$cubic_mean = Average::cubicMean($numbers);
// Truncated mean (trimmed mean)
$trim_percent = 25;
$truncated_mean = Average::truncatedMean($numbers, $trim_percent);
// Generalized mean (power mean)
$p = 2;
$generalized_mean = Average::generalizedMean($numbers, $p); // same as powerMean
$power_mean = Average::powerMean($numbers, $p); // same as generalizedMean// Lehmer mean
$p = 3;
$lehmer_mean = Average::lehmerMean($numbers, $p);
// Moving averages
$n = 3;
$weights = [3, 2, 1];
$SMA = Average::simpleMovingAverage($numbers, $n); // 3 n-point moving average
$CMA = Average::cumulativeMovingAverage($numbers);
$WMA = Average::weightedMovingAverage($numbers, $n, $weights);
$EPA = Average::exponentialMovingAverage($numbers, $n);
// Means of two numbers
list($x, $y) = [24, 6];
$agm = Average::arithmeticGeometricMean($x, $y); // same as agm
$agm = Average::agm($x, $y); // same as arithmeticGeometricMean
$log_mean = Average::logarithmicMean($x, $y);
$heronian_mean = Average::heronianMean($x, $y);
$identric_mean = Average::identricMean($x, $y);
// Averages report
$averages = Average::describe($numbers);
print_r($averages);
/* Array ( [mean] => 15 [median] => 14 [mode] => Array ( [0] => 13 ) [geometric_mean] => 14.789726414533 [harmonic_mean] => 14.605077399381 [contraharmonic_mean] => 15.474074074074 [quadratic_mean] => 15.235193176035 [trimean] => 14.5 [iqm] => 14 [cubic_mean] => 15.492307432707) */
use MathPHP\Statistics\KernelDensityEstimation
$data = [-2.76, -1.09, -0.5, -0.15, 0.22, 0.69, 1.34, 1.75];
$x = 0.5;
// Density estimator with default bandwidth (normal distribution approximation) and kernel function (standard normal)
$kde = new KernelDensityEstimation($data);
$density = $kde->evaluate($x)
// Custom bandwidth
$h = 0.1;
$kde->setBandwidth($h);
// Library of built-in kernel functions
$kde->setKernelFunction(KernelDensityEstimation::STANDARD_NORMAL);
$kde->setKernelFunction(KernelDensityEstimation::NORMAL);
$kde->setKernelFunction(KernelDensityEstimation::UNIFORM);
$kde->setKernelFunction(KernelDensityEstimation::TRIANGULAR);
$kde->setKernelFunction(KernelDensityEstimation::EPANECHNIKOV);
$kde->setKernelFunction(KernelDensityEstimation::TRICUBE);
// Set custom kernel function (user-provided callable)
$kernel = function ($x) {
if (abs($x) > 1) {
return 0;
} else {
return 70 / 81 * ((1 - abs($x) ** 3) ** 3);
}
};
$kde->setKernelFunction($kernel);
// All customization optionally can be done in the constructor
$kde = new KernelDesnsityEstimation($data, $h, $kernel);
Statistics – Random Variables
use MathPHP\Statistics\RandomVariable;
$X = [1, 2, 3, 4];
$Y = [2, 3, 4, 5];
// Central moment (nth moment)
$second_central_moment = RandomVariable::centralMoment($X, 2);
$third_central_moment = RandomVariable::centralMoment($X, 3);
// Skewness (population and sample)
$skewness = RandomVariable::skewness($X); // general method of calculating skewness
$skewness = RandomVariable::populationSkewness($X); // similar to Excel's SKEW.P
$skewness = RandomVariable::sampleSkewness($X); // similar to Excel's SKEW
$SES = RandomVariable::ses(count($X)); // standard error of skewness// Kurtosis (excess)
$kurtosis = RandomVariable::kurtosis($X);
$platykurtic = RandomVariable::isPlatykurtic($X); // true if kurtosis is less than zero
$leptokurtic = RandomVariable::isLeptokurtic($X); // true if kurtosis is greater than zero
$mesokurtic = RandomVariable::isMesokurtic($X); // true if kurtosis is zero
$SEK = RandomVariable::sek(count($X)); // standard error of kurtosis// Standard error of the mean (SEM)
$sem = RandomVariable::standardErrorOfTheMean($X); // same as sem
$sem = RandomVariable::sem($X); // same as standardErrorOfTheMean// Confidence interval
$μ = 90; // sample mean
$n = 9; // sample size
$σ = 36; // standard deviation
$cl = 99; // confidence level
$ci = RandomVariable::confidenceInterval($μ, $n, $σ, $cl); // Array( [ci] => 30.91, [lower_bound] => 59.09, [upper_bound] => 120.91 )
Statistics – Regressions
use MathPHP\Statistics\Regression;
$points = [[1,2], [2,3], [4,5], [5,7], [6,8]];
// Simple linear regression (least squares method)
$regression = new Regression\Linear($points);
$parameters = $regression->getParameters(); // [m => 1.2209302325581, b => 0.6046511627907]
$equation = $regression->getEquation(); // y = 1.2209302325581x + 0.6046511627907
$y = $regression->evaluate(5); // Evaluate for y at x = 5 using regression equation
$ci = $regression->ci(5, 0.5); // Confidence interval for x = 5 with p-value of 0.5
$pi = $regression->pi(5, 0.5); // Prediction interval for x = 5 with p-value of 0.5; Optional number of trials parameter.
$Ŷ = $regression->yHat();
$r = $regression->r(); // same as correlationCoefficient
$r² = $regression->r2(); // same as coefficientOfDetermination
$se = $regression->standardErrors(); // [m => se(m), b => se(b)]
$t = $regression->tValues(); // [m => t, b => t]
$p = $regression->tProbability(); // [m => p, b => p]
$F = $regression->fStatistic();
$p = $regression->fProbability();
$h = $regression->leverages();
$e = $regression->residuals();
$D = $regression->cooksD();
$DFFITS = $regression->dffits();
$SStot = $regression->sumOfSquaresTotal();
$SSreg = $regression->sumOfSquaresRegression();
$SSres = $regression->sumOfSquaresResidual();
$MSR = $regression->meanSquareRegression();
$MSE = $regression->meanSquareResidual();
$MSTO = $regression->meanSquareTotal();
$error = $regression->errorSd(); // Standard error of the residuals
$V = $regression->regressionVariance();
$n = $regression->getSampleSize(); // 5
$points = $regression->getPoints(); // [[1,2], [2,3], [4,5], [5,7], [6,8]]
$xs = $regression->getXs(); // [1, 2, 4, 5, 6]
$ys = $regression->getYs(); // [2, 3, 5, 7, 8]
$ν = $regression->degreesOfFreedom();
// Linear regression through a fixed point (least squares method)
$force_point = [0,0];
$regression = new Regression\LinearThroughPoint($points, $force_point);
$parameters = $regression->getParameters();
$equation = $regression->getEquation();
$y = $regression->evaluate(5);
$Ŷ = $regression->yHat();
$r = $regression->r();
$r² = $regression->r2();
⋮ ⋮
// Theil–Sen estimator (Sen's slope estimator, Kendall–Theil robust line)
$regression = new Regression\TheilSen($points);
$parameters = $regression->getParameters();
$equation = $regression->getEquation();
$y = $regression->evaluate(5);
⋮ ⋮
// Use Lineweaver-Burk linearization to fit data to the Michaelis–Menten model: y = (V * x) / (K + x)
$regression = new Regression\LineweaverBurk($points);
$parameters = $regression->getParameters(); // [V, K]
$equation = $regression->getEquation(); // y = Vx / (K + x)
$y = $regression->evaluate(5);
⋮ ⋮
// Use Hanes-Woolf linearization to fit data to the Michaelis–Menten model: y = (V * x) / (K + x)
$regression = new Regression\HanesWoolf($points);
$parameters = $regression->getParameters(); // [V, K]
$equation = $regression->getEquation(); // y = Vx / (K + x)
$y = $regression->evaluate(5);
⋮ ⋮
// Power law regression - power curve (least squares fitting)
$regression = new Regression\PowerLaw($points);
$parameters = $regression->getParameters(); // [a => 56.483375436574, b => 0.26415375648621]
$equation = $regression->getEquation(); // y = 56.483375436574x^0.26415375648621
$y = $regression->evaluate(5);
⋮ ⋮
// LOESS - Locally Weighted Scatterplot Smoothing (Local regression)
$α = 1/3; // Smoothness parameter
$λ = 1; // Order of the polynomial fit
$regression = new Regression\LOESS($points, $α, $λ);
$y = $regression->evaluate(5);
$Ŷ = $regression->yHat();
⋮ ⋮
Statistics – Significance Testing
use MathPHP\Statistics\Significance;
// Z test - One sample (z and p values)
$Hₐ = 20; // Alternate hypothesis (M Sample mean)
$n = 200; // Sample size
$H₀ = 19.2; // Null hypothesis (μ Population mean)
$σ = 6; // SD of population (Standard error of the mean)
$z = Significance:zTest($Hₐ, $n, $H₀, $σ); // Same as zTestOneSample
$z = Significance:zTestOneSample($Hₐ, $n, $H₀, $σ); // Same as zTest/* [ 'z' => 1.88562, // Z score 'p1' => 0.02938, // one-tailed p value 'p2' => 0.0593, // two-tailed p value] */// Z test - Two samples (z and p values)
$μ₁ = 27; // Sample mean of population 1
$μ₂ = 33; // Sample mean of population 2
$n₁ = 75; // Sample size of population 1
$n₂ = 50; // Sample size of population 2
$σ₁ = 14.1; // Standard deviation of sample mean 1
$σ₂ = 9.5; // Standard deviation of sample mean 2
$z = Significance::zTestTwoSample($μ₁, $μ₂, $n₁, $n₂, $σ₁, $σ₂);
/* [ 'z' => -2.36868418147285, // z score 'p1' => 0.00893, // one-tailed p value 'p2' => 0.0179, // two-tailed p value] */// Z score
$M = 8; // Sample mean
$μ = 7; // Population mean
$σ = 1; // Population SD
$z = Significance::zScore($M, $μ, $σ);
// T test - One sample (from sample data)
$a = [3, 4, 4, 5, 5, 5, 6, 6, 7, 8]; // Data set
$H₀ = 300; // Null hypothesis (μ₀ Population mean)
$tTest = Significance::tTest($a, $H₀)
print_r($tTest);
/* Array ( [t] => 0.42320736951516 // t score [df] => 9 // degrees of freedom [p1] => 0.34103867713806 // one-tailed p value [p2] => 0.68207735427613 // two-tailed p value [mean] => 5.3 // sample mean [sd] => 1.4944341180973 // standard deviation) */// T test - One sample (from summary data)
$Hₐ = 280; // Alternate hypothesis (M Sample mean)
$s = 50; // Standard deviation of sample
$n = 15; // Sample size
$H₀ = 300; // Null hypothesis (μ₀ Population mean)
$tTest = Significance::tTestOneSampleFromSummaryData($Hₐ, $s, $n, $H₀);
print_r($tTest);
/* Array ( [t] => -1.549193338483 // t score [df] => 14 // degreees of freedom [p1] => 0.071820000122611 // one-tailed p value [p2] => 0.14364000024522 // two-tailed p value [mean] => 280 // sample mean [sd] => 50 // standard deviation) */// T test - Two samples (from sample data)
$x₁ = [27.5, 21.0, 19.0, 23.6, 17.0, 17.9, 16.9, 20.1, 21.9, 22.6, 23.1, 19.6, 19.0, 21.7, 21.4];
$x₂ = [27.1, 22.0, 20.8, 23.4, 23.4, 23.5, 25.8, 22.0, 24.8, 20.2, 21.9, 22.1, 22.9, 20.5, 24.4];
$tTest = Significance::tTest($x₁, $x₂);
print_r($tTest);
/* Array ( [t] => -2.4553600286929 // t score [df] => 24.988527070145 // degrees of freedom [p1] => 0.010688914613979 // one-tailed p value [p2] => 0.021377829227958 // two-tailed p value [mean1] => 20.82 // mean of sample x₁ [mean2] => 22.98667 // mean of sample x₂ [sd1] => 2.804894 // standard deviation of x₁ [sd2] => 1.952605 // standard deviation of x₂) */// T test - Two samples (from summary data)
$μ₁ = 42.14; // Sample mean of population 1
$μ₂ = 43.23; // Sample mean of population 2
$n₁ = 10; // Sample size of population 1
$n₂ = 10; // Sample size of population 2
$σ₁ = 0.683; // Standard deviation of sample mean 1
$σ₂ = 0.750; // Standard deviation of sample mean 2
$tTest = Significance::tTestTwoSampleFromSummaryData($μ₁, $μ₂, $n₁, $n₂, $σ₁, $σ₂);
print_r($tTest);
/* Array ( [t] => -3.3972305988708 // t score [df] => 17.847298548027 // degrees of freedom [p1] => 0.0016211251126198 // one-tailed p value [p2] => 0.0032422502252396 // two-tailed p value [mean1] => 42.14 [mean2] => 43.23 [sd1] => 0.6834553 [sd2] => 0.7498889] */// T score
$Hₐ = 280; // Alternate hypothesis (M Sample mean)
$s = 50; // SD of sample
$n = 15; // Sample size
$H₀ = 300; // Null hypothesis (μ₀ Population mean)
$t = Significance::tScore($Hₐ, $s, $n, $H);
// χ² test (chi-squared goodness of fit test)
$observed = [4, 6, 17, 16, 8, 9];
$expected = [10, 10, 10, 10, 10, 10];
$χ² = Significance::chiSquaredTest($observed, $expected);
// ['chi-square' => 14.2, 'p' => 0.014388]
Trigonometry
use MathPHP\Trigonometry;
$n = 9;
$points = Trigonometry::unitCircle($n); // Produce n number of points along the unit circle
Unit Tests
Beyond 100% code coverage!
MathPHP has thousands of unit tests testing individual functions directly with numerous data inputs to achieve 100% test coverage. MathPHP unit tests also test mathematical axioms which indirectly test the same functions in multiple different ways ensuring that those math properties all work out according to the axioms.
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