diff options
Diffstat (limited to 'numpy/oldnumeric/random_array.py')
| -rw-r--r-- | numpy/oldnumeric/random_array.py | 56 |
1 files changed, 28 insertions, 28 deletions
diff --git a/numpy/oldnumeric/random_array.py b/numpy/oldnumeric/random_array.py index 4dcddda12..ecb3d0b23 100644 --- a/numpy/oldnumeric/random_array.py +++ b/numpy/oldnumeric/random_array.py @@ -1,7 +1,7 @@ """Backward compatible module for RandomArray """ -from __future__ import division, absolute_import +from __future__ import division, absolute_import, print_function __all__ = ['ArgumentError','F','beta','binomial','chi_square', 'exponential', 'gamma', 'get_seed', 'mean_var_test', 'multinomial', @@ -192,12 +192,12 @@ def mean_var_test(x, type, mean, var, skew=[]): 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) - 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 + 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.) - print "Skewness of those random numbers (should be about ", skew, "):", x_skew + print("Skewness of those random numbers (should be about ", skew, "):", x_skew) def test(): obj = mt.get_state() @@ -205,25 +205,25 @@ def test(): obj2 = mt.get_state() 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. + print("First random number is", random()) + 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. + 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,) if np.minimum.reduce(y) <= 0.5 or np.maximum.reduce(y) >= 0.6: raise SystemExit("uniform returned out of desired range") - 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 "random_integers(10, shape=[20])" - print random_integers(10, shape=[20]) + 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("random_integers(10, shape=[20])") + print(random_integers(10, shape=[20])) s = 3.0 x = normal(2.0, s, [10, 1000]) if len(x.shape) != 2 or x.shape[0] != 10 or x.shape[1] != 1000: @@ -233,19 +233,19 @@ def test(): 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]))) - print "\nA multivariate normal", x + 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]) - print "A 4x3x2 array containing multivariate normals" - print x + 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. - print "Average of 10000 multivariate normals with mean [-100,0,100]" - print x_mean + 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("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.) 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) @@ -256,14 +256,14 @@ def test(): mean_var_test(x, "F random numbers with 5 and 10 degrees of freedom", 1.25, 1.35) x = poisson(50., 10000) mean_var_test(x, "poisson random numbers with mean 50", 50, 50, 0.14) - print "\nEach element is the result of 16 binomial trials with probability 0.5:" - print binomial(16, 0.5, 16) - print "\nEach element is the result of 16 negative binomial trials with probability 0.5:" - print negative_binomial(16, 0.5, [16,]) - print "\nEach row is the result of 16 multinomial trials with probabilities [0.1, 0.5, 0.1 0.3]:" + print("\nEach element is the result of 16 binomial trials with probability 0.5:") + print(binomial(16, 0.5, 16)) + print("\nEach element is the result of 16 negative binomial trials with probability 0.5:") + print(negative_binomial(16, 0.5, [16,])) + 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(x) + print("Mean = ", np.sum(x,axis=0)/8.) if __name__ == '__main__': test() |