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How to use is_not_zero method in assertpy

Best Python code snippet using assertpy_python

math.py

Source:math.py

...53  ["comm-mul",   Mul(a, b),          Mul(b, a)],54  ["assoc-add",  Add(a, Add(b, c)),  Add(Add(a, b), c)],55  ["assoc-mul",  Mul(a, Mul(b, c)),  Mul(Mul(a, b), c)],56  ["sub-canon",  Sub(a, b),  Add(a, Mul(-1, b))],57  # rw!("div-canon"; "(/ ?a ?b)" => "(* ?a (pow ?b -1))" if is_not_zero("?b")),58  # // rw!("canon-sub"; "(+ ?a (* -1 ?b))"  => "(- ?a ?b)"),59  # // rw!("canon-div"; "(* ?a (pow ?b -1))" => "(/ ?a ?b)" if is_not_zero("?b")),60  ["zero-add",  Add(a, 0),  a],61  ["zero-mul",  Mul(a, 0),  0],62  ["one-mul",   Mul(a, 1),  a],63  ["add-zero",  a,  Add(a, 0)],64  ["mul-one",   a,  Mul(a, 1)],65  ["cancel-sub",  Sub(a, a),  0],66  # rw!("cancel-div"; "(/ ?a ?a)" => "1" if is_not_zero("?a")),67  ["distribute",  Mul(a, Add(b, c)),          Add(Mul(a, b), Mul(a, c))],68  ["factor",      Add(Mul(a, b), Mul(a, c)),  Mul(a, Add(b, c))],69  ["pow-mul",  Mul(Pow(a, b), Pow(a, c)),  Pow(a, Add(b, c))],70  # rw!("pow0"; "(pow ?x 0)" => "1"71  #            if is_not_zero("?x")),72  ["pow1",     Pow(x, 1),                  x],73  ["pow2",     Pow(x, 2),                  Mul(x, x)],74  # rw!("pow-recip"; "(pow ?x -1)" => "(/ 1 ?x)"75  #            if is_not_zero("?x")),76  # rw!("recip-mul-div"; "(* ?x (/ 1 ?x))" => "1" if is_not_zero("?x")),77  # rw!("d-variable"; "(d ?x ?x)" => "1" if is_sym("?x")),78  # rw!("d-constant"; "(d ?x ?c)" => "0" if is_sym("?x") if is_const_or_distinct_var("?c", "?x")),79  ["d-add",  Diff(x, Add(a, b)),  Add(Diff(x, a), Diff(x, b))],80  ["d-mul",  Diff(x, Mul(a, b)),  Add(Mul(a, Diff(x, b)), Mul(b, Diff(x, a)))],81  ["d-sin",  Diff(x, Sin(x)),  Cos(x)],82  ["d-cos",  Diff(x, Cos(x)),  Mul(-1, Sin(x))],83  # rw!("d-ln"; "(d ?x (ln ?x))" => "(/ 1 ?x)" if is_not_zero("?x")),84  # rw!("d-power";85  #     "(d ?x (pow ?f ?g))" =>86  #     "(* (pow ?f ?g)87  #         (+ (* (d ?x ?f)88  #               (/ ?g ?f))89  #            (* (d ?x ?g)90  #               (ln ?f))))"91  #     if is_not_zero("?f")92  #     if is_not_zero("?g")93  # ),94  ["i-one",    Integral(1, x),          x],95  # rw!("i-power-const"; "(i (pow ?x ?c) ?x)" =>96  #            "(/ (pow ?x (+ ?c 1)) (+ ?c 1))" if is_const("?c")),97  ["i-cos",    Integral(Cos(x), x),     Sin(x)],98  ["i-sin",    Integral(Sin(x), x),     Mul(-1, Cos(x))],99  ["i-sum",    Integral(Add(f, g), x),  Add(Integral(f, x), Integral(g, x))],100  ["i-dif",    Integral(Sub(f, g), x),  Sub(Integral(f, x), Integral(g, x))],101  ["i-parts",  Integral(Mul(a, b), x),102               Sub(Mul(a, Integral(b, x)), Integral(Mul(Diff(x, a), Integral(b, x)), x))],103]104# Turn the lists into rewrites105rules = list()106for l in list_rules:...

multi_normal.py

Source:multi_normal.py

1import numpy as np2import scipy.stats3from core.utils import verification4@verification('a1', 'aa', 'a1')5def pdf(mean: np.ndarray, cov: np.ndarray, x: np.ndarray) -> float:6    """7    Computes the pdf of a multivariate normal at the specified location.8    """9    p = scipy.stats.multivariate_normal.pdf(x.flatten(), mean.flatten(), cov, allow_singular=True)10    return p11@verification('a1', 'aa', 'ba', 'aa')12def posterior(mean: np.ndarray, cov:np.ndarray, x: np.ndarray, cov_known: np.ndarray) -> (np.ndarray, np.ndarray):13    """14    Computes the parameters of the posterior distribution in case the true covariance matrix is known.15    """16    n = len(x)17    mean_x = x.mean(axis=0)[:, None]18    inverse = np.linalg.inv(cov + (1 / n) * cov_known)19    mu_post = cov @ inverse * mean_x + (1 / n) * cov_known @ inverse @ mean20    cov_post = cov @ inverse @ cov_known * (1 / n)21    return np.diag(mu_post)[:, None], cov_post22@verification('a1', 'aa', 'a1', 'aa')23def kl_divergence(mean1: np.ndarray, cov1: np.ndarray, mean2: np.ndarray, cov2: np.ndarray) -> float:24    """25    Computes the KL-Divergence between two multivariate normal distributions.26    """27    # Removing zeros in diagonal of covariance matrix28    is_not_zero = (np.diag(cov1) != 0) & (np.diag(cov2) != 0)29    mean1 = mean1[is_not_zero, :]30    mean2 = mean2[is_not_zero, :]31    cov1 = cov1[is_not_zero, :][:, is_not_zero]32    cov2 = cov2[is_not_zero, :][:, is_not_zero]33    # Computing KL-divergence34    n = len(mean1)35    return 1 / 2 * (np.log(np.linalg.det(cov2) / np.linalg.det(cov1)) - n +36                    np.trace(np.linalg.inv(cov2) @ cov1) +37                    (mean2 - mean1).T @ np.linalg.inv(cov2) @ (mean2 - mean1))[0][0]38@verification('a1', 'ba')39def ttest_1sample(mean: np.ndarray, x: np.ndarray) -> float:  # TODO: Check computation40    x = np.asarray(x)41    nobs, k_vars = x.shape42    mean_x = x.mean(0)43    cov = np.cov(x, rowvar=False, ddof=1)44    diff = mean_x - mean.flatten()45    t2 = nobs * diff.dot(np.linalg.solve(cov, diff))46    factor = (nobs - 1) * k_vars / (nobs - k_vars)47    statistic = t2 / factor48    df = (k_vars, nobs - k_vars)49    pvalue = scipy.stats.f.sf(statistic, df[0], df[1])50    return pvalue51if __name__ == '__main__':...

million.py

Source:million.py

1num = 10000002is_not_zero = True3iterated = 04while is_not_zero:5    num //= 26    iterated += 17    if num == 0:8        is_not_zero = False...

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