# 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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