Examples with LinearRegression suite.math.numeric.Statistic.LinearRegression used on opensource projects

Example 1 with LinearRegression

use of suite.math.numeric.Statistic.LinearRegression in project suite by stupidsing.

the class Arch method arch.

public float[] arch(float[] ys, int p, int q) {
    // auto regressive
    int length = ys.length;
    float[][] xs0 = To.array(length, float[].class, i -> copyPadZeroes(ys, i - p, i));
    LinearRegression lr0 = stat.linearRegression(xs0, ys, null);
    float[] variances = To.vector(lr0.residuals, residual -> residual * residual);
    // conditional heteroskedasticity
    LinearRegression lr1 = stat.linearRegression(// 
    Ints_.range(// 
    length).map(i -> FltObjPair.of(variances[i], copyPadZeroes(variances, i - p, i))));
    return Floats_.concat(lr0.coefficients, lr1.coefficients);
}

Example 2 with LinearRegression

use of suite.math.numeric.Statistic.LinearRegression in project suite by stupidsing.

the class FactorLr method ols.

private LinearRegression ols(DataSource rds0, TimeRange period) {
    DataSource ys = rds0.range(period);
    float[][] returns_ = // 
    Read.from(// 
    indexPrices).map(// 
    prices -> DataSource.of(timestamps, prices).range(period).alignBeforePrices(ys.ts).returns()).toArray(float[].class);
    float[][] xs = mtx.transpose(returns_);
    return stat.linearRegression(xs, ys.returns(), indexSymbols.toArray(String.class));
}

Example 3 with LinearRegression

use of suite.math.numeric.Statistic.LinearRegression in project suite by stupidsing.

the class StatisticTest method testLinearRegression.

@Test
public void testLinearRegression() {
    int m = 7, n = 9;
    float[] expect = Floats_.toArray(m, j -> random.nextFloat());
    float[][] xs = To.matrix(n, m, (i, j) -> random.nextFloat());
    LinearRegression lr = stat.linearRegression(// 
    Read.from(// 
    xs).map(x -> FltObjPair.of((float) (vec.dot(expect, x) + random.nextGaussian() * .01f), x)));
    Dump.out(lr);
    float[] actual = lr.coefficients();
    vec.verifyEquals(expect, actual, .1f);
    float[] xtest = Floats_.toArray(m, j -> random.nextFloat());
    MathUtil.verifyEquals(vec.dot(expect, xtest), lr.predict(xtest), .1f);
    MathUtil.verifyEquals(1f, (float) lr.r2, .1f);
}

Example 4 with LinearRegression

use of suite.math.numeric.Statistic.LinearRegression in project suite by stupidsing.

the class PairTest method test.

private void test(TimeRange period, String symbol0, String symbol1) {
    DataSource ds0 = cfg.dataSource(symbol0, period);
    DataSource ds1 = cfg.dataSource(symbol1, period);
    LngStreamlet ts0 = Longs_.of(ds0.ts);
    LngStreamlet ts1 = Longs_.of(ds1.ts);
    long[] tradeTimes = Longs_.concat(ts0, ts1).distinct().sort().toArray();
    float[] prices0 = ds0.alignBeforePrices(tradeTimes).prices;
    float[] prices1 = ds1.alignBeforePrices(tradeTimes).prices;
    int length = prices0.length;
    LinearRegression lr = statistic.linearRegression(// 
    Ints_.range(// 
    length).map(i -> FltObjPair.of(prices1[i], new float[] { prices0[i], 1f })));
    System.out.println(symbol0 + " -> " + symbol1 + lr);
    assertTrue(.4d < lr.r2);
}

Example 5 with LinearRegression

use of suite.math.numeric.Statistic.LinearRegression in project suite by stupidsing.

the class Arima method maIa.

// "High Frequency Trading - A Practical Guide to Algorithmic Strategies and
// Trading Systems", Irene Aldridge, page 100
// xs[t]
// = mas[0] * 1 + mas[1] * ep[t - 1] + ... + mas[q] * ep[t - q]
// + ep[t]
@SuppressWarnings("unused")
private float[] maIa(float[] xs, int q) {
    int length = xs.length;
    float[][] epqByIter = new float[q][];
    int iter = 0;
    int qm1 = q - 1;
    while (true) {
        int iter_ = iter;
        LinearRegression lr = stat.linearRegression(// 
        Ints_.range(// 
        length).map(t -> {
            int tqm1 = t + qm1;
            float[] lrxs = Floats_.concat(Floats_.of(1f), Ints_.range(iter_).collect(Int_Flt.lift(i -> epqByIter[i][tqm1 - i]))).toArray();
            return FltObjPair.of(xs[t], lrxs);
        }));
        if (iter < q)
            System.arraycopy(lr.residuals, 0, epqByIter[iter++] = new float[q + length], q, length);
        else
            return lr.coefficients();
    }
}