diff options
Diffstat (limited to 'numpy/oldnumeric/random_array.py')
| -rw-r--r-- | numpy/oldnumeric/random_array.py | 34 |
1 files changed, 17 insertions, 17 deletions
diff --git a/numpy/oldnumeric/random_array.py b/numpy/oldnumeric/random_array.py index ecb3d0b23..c43a49cdb 100644 --- a/numpy/oldnumeric/random_array.py +++ b/numpy/oldnumeric/random_array.py @@ -3,7 +3,7 @@ """ from __future__ import division, absolute_import, print_function -__all__ = ['ArgumentError','F','beta','binomial','chi_square', 'exponential', +__all__ = ['ArgumentError', 'F', 'beta', 'binomial', 'chi_square', 'exponential', 'gamma', 'get_seed', 'mean_var_test', 'multinomial', 'multivariate_normal', 'negative_binomial', 'noncentral_F', 'noncentral_chi_square', 'normal', 'permutation', 'poisson', @@ -19,7 +19,7 @@ def seed(x=0, y=0): if (x == 0 or y == 0): mt.seed() else: - mt.seed((x,y)) + mt.seed((x, y)) def get_seed(): raise NotImplementedError( @@ -189,14 +189,14 @@ def poisson(mean, shape=[]): def mean_var_test(x, type, mean, var, skew=[]): n = len(x) * 1.0 - x_mean = np.sum(x,axis=0)/n + x_mean = np.sum(x, axis=0)/n x_minus_mean = x - x_mean - x_var = np.sum(x_minus_mean*x_minus_mean,axis=0)/(n-1.0) + x_var = np.sum(x_minus_mean*x_minus_mean, axis=0)/(n-1.0) print("\nAverage of ", len(x), type) print("(should be about ", mean, "):", x_mean) print("Variance of those random numbers (should be about ", var, "):", x_var) if skew != []: - x_skew = (np.sum(x_minus_mean*x_minus_mean*x_minus_mean,axis=0)/9998.)/x_var**(3./2.) + x_skew = (np.sum(x_minus_mean*x_minus_mean*x_minus_mean, axis=0)/9998.)/x_var**(3./2.) print("Skewness of those random numbers (should be about ", skew, "):", x_skew) def test(): @@ -206,13 +206,13 @@ def test(): if (obj2[1] - obj[1]).any(): raise SystemExit("Failed seed test.") print("First random number is", random()) - print("Average of 10000 random numbers is", np.sum(random(10000),axis=0)/10000.) - x = random([10,1000]) + print("Average of 10000 random numbers is", np.sum(random(10000), axis=0)/10000.) + x = random([10, 1000]) if len(x.shape) != 2 or x.shape[0] != 10 or x.shape[1] != 1000: raise SystemExit("random returned wrong shape") x.shape = (10000,) - print("Average of 100 by 100 random numbers is", np.sum(x,axis=0)/10000.) - y = uniform(0.5,0.6, (1000,10)) + print("Average of 100 by 100 random numbers is", np.sum(x, axis=0)/10000.) + y = uniform(0.5, 0.6, (1000, 10)) if len(y.shape) !=2 or y.shape[0] != 1000 or y.shape[1] != 10: raise SystemExit("uniform returned wrong shape") y.shape = (10000,) @@ -221,7 +221,7 @@ def test(): print("randint(1, 10, shape=[50])") print(randint(1, 10, shape=[50])) print("permutation(10)", permutation(10)) - print("randint(3,9)", randint(3,9)) + print("randint(3,9)", randint(3, 9)) print("random_integers(10, shape=[20])") print(random_integers(10, shape=[20])) s = 3.0 @@ -232,20 +232,20 @@ def test(): mean_var_test(x, "normally distributed numbers with mean 2 and variance %f"%(s**2,), 2, s**2, 0) x = exponential(3, 10000) mean_var_test(x, "random numbers exponentially distributed with mean %f"%(s,), s, s**2, 2) - x = multivariate_normal(np.array([10,20]), np.array(([1,2],[2,4]))) + x = multivariate_normal(np.array([10, 20]), np.array(([1, 2], [2, 4]))) print("\nA multivariate normal", x) if x.shape != (2,): raise SystemExit("multivariate_normal returned wrong shape") - x = multivariate_normal(np.array([10,20]), np.array([[1,2],[2,4]]), [4,3]) + x = multivariate_normal(np.array([10, 20]), np.array([[1, 2], [2, 4]]), [4, 3]) print("A 4x3x2 array containing multivariate normals") print(x) - if x.shape != (4,3,2): raise SystemExit("multivariate_normal returned wrong shape") - x = multivariate_normal(np.array([-100,0,100]), np.array([[3,2,1],[2,2,1],[1,1,1]]), 10000) - x_mean = np.sum(x,axis=0)/10000. + if x.shape != (4, 3, 2): raise SystemExit("multivariate_normal returned wrong shape") + x = multivariate_normal(np.array([-100, 0, 100]), np.array([[3, 2, 1], [2, 2, 1], [1, 1, 1]]), 10000) + x_mean = np.sum(x, axis=0)/10000. print("Average of 10000 multivariate normals with mean [-100,0,100]") print(x_mean) x_minus_mean = x - x_mean print("Estimated covariance of 10000 multivariate normals with covariance [[3,2,1],[2,2,1],[1,1,1]]") - print(np.dot(np.transpose(x_minus_mean),x_minus_mean)/9999.) + print(np.dot(np.transpose(x_minus_mean), x_minus_mean)/9999.) x = beta(5.0, 10.0, 10000) mean_var_test(x, "beta(5.,10.) random numbers", 0.333, 0.014) x = gamma(.01, 2., 10000) @@ -263,7 +263,7 @@ def test(): print("\nEach row is the result of 16 multinomial trials with probabilities [0.1, 0.5, 0.1 0.3]:") x = multinomial(16, [0.1, 0.5, 0.1], 8) print(x) - print("Mean = ", np.sum(x,axis=0)/8.) + print("Mean = ", np.sum(x, axis=0)/8.) if __name__ == '__main__': test() |